OpenCVForUnity.XphotoModule.Xphoto Class Reference

Static Public Member Functions
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize, int searchWindowSize)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h, int templateWindowSize)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2, float h)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dstStep1, Mat dstStep2)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step, int transformType)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType, int step)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta, int normType)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep, float beta)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize, int slidingStep)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2, int groupSize)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1, int blockMatchingStep2)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize, int blockMatchingStep1)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize, int searchWindowSize)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h, int templateWindowSize)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst, float h)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	bm3dDenoising (Mat src, Mat dst)
	Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise. More...
static void	dctDenoising (Mat src, Mat dst, double sigma, int psize)
	The function implements simple dct-based denoising. More...
static void	dctDenoising (Mat src, Mat dst, double sigma)
	The function implements simple dct-based denoising. More...
static void	inpaint (Mat src, Mat mask, Mat dst, int algorithmType)
	The function implements different single-image inpainting algorithms. More...
static void	oilPainting (Mat src, Mat dst, int size, int dynRatio, int code)
	oilPainting See the book [Holzmann1988] for details. More...
static void	oilPainting (Mat src, Mat dst, int size, int dynRatio)
	oilPainting See the book [Holzmann1988] for details. More...
static TonemapDurand	createTonemapDurand (float gamma, float contrast, float saturation, float sigma_color, float sigma_space)
	Creates TonemapDurand object. More...
static TonemapDurand	createTonemapDurand (float gamma, float contrast, float saturation, float sigma_color)
	Creates TonemapDurand object. More...
static TonemapDurand	createTonemapDurand (float gamma, float contrast, float saturation)
	Creates TonemapDurand object. More...
static TonemapDurand	createTonemapDurand (float gamma, float contrast)
	Creates TonemapDurand object. More...
static TonemapDurand	createTonemapDurand (float gamma)
	Creates TonemapDurand object. More...
static TonemapDurand	createTonemapDurand ()
	Creates TonemapDurand object. More...
static SimpleWB	createSimpleWB ()
	Creates an instance of SimpleWB. More...
static GrayworldWB	createGrayworldWB ()
	Creates an instance of GrayworldWB. More...
static LearningBasedWB	createLearningBasedWB (string path_to_model)
	Creates an instance of LearningBasedWB. More...
static LearningBasedWB	createLearningBasedWB ()
	Creates an instance of LearningBasedWB. More...
static void	applyChannelGains (Mat src, Mat dst, float gainB, float gainG, float gainR)
	Implements an efficient fixed-point approximation for applying channel gains, which is the last step of multiple white balance algorithms. More...

Public Attributes
const int	BM3D_STEPALL = 0
const int	BM3D_STEP1 = 1
const int	BM3D_STEP2 = 2
const int	INPAINT_SHIFTMAP = 0
const int	INPAINT_FSR_BEST = 1
const int	INPAINT_FSR_FAST = 2
const int	HAAR = 0

Member Function Documentation

applyChannelGains()

static void OpenCVForUnity.XphotoModule.Xphoto.applyChannelGains ( Mat  src, Mat  dst, float  gainB, float  gainG, float  gainR  )

static

Implements an efficient fixed-point approximation for applying channel gains, which is the last step of multiple white balance algorithms.

Parameters

src	Input three-channel image in the BGR color space (either CV_8UC3 or CV_16UC3)
dst	Output image of the same size and type as src.
gainB	gain for the B channel
gainG	gain for the G channel
gainR	gain for the R channel

bm3dDenoising() [1/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta, int  normType, int  step, int  transformType  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [2/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta, int  normType, int  step  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [3/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta, int  normType  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [4/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [5/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [6/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [7/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [8/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [9/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize, int  searchWindowSize  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [10/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h, int  templateWindowSize  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [11/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2, float  h  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [12/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dstStep1, Mat  dstStep2  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dstStep1	Output image of the first step of BM3D with the same size and type as src.
dstStep2	Output image of the second step of BM3D with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [13/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta, int  normType, int  step, int  transformType  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [14/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta, int  normType, int  step  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [15/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta, int  normType  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [16/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep, float  beta  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [17/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize, int  slidingStep  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [18/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2, int  groupSize  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [19/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1, int  blockMatchingStep2  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [20/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize, int  blockMatchingStep1  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [21/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize, int  searchWindowSize  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [22/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h, int  templateWindowSize  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [23/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst, float  h  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

bm3dDenoising() [24/24]

static void OpenCVForUnity.XphotoModule.Xphoto.bm3dDenoising ( Mat  src, Mat  dst  )

static

Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf&gt; with several computational optimizations. Noise expected to be a gaussian white noise.

Parameters

src	Input 8-bit or 16-bit 1-channel image.
dst	Output image with the same size and type as src.
h	Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise.
templateWindowSize	Size in pixels of the template patch that is used for block-matching. Should be power of 2.
searchWindowSize	Size in pixels of the window that is used to perform block-matching. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize.
blockMatchingStep1	Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
blockMatchingStep2	Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance.
groupSize	Maximum size of the 3D group for collaborative filtering.
slidingStep	Sliding step to process every next reference block.
beta	Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero.
normType	Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results.
step	Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present.
transformType	Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported.

This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.

See also

fastNlMeansDenoising

createGrayworldWB()

static GrayworldWB OpenCVForUnity.XphotoModule.Xphoto.createGrayworldWB ( )

static

Creates an instance of GrayworldWB.

createLearningBasedWB() [1/2]

static LearningBasedWB OpenCVForUnity.XphotoModule.Xphoto.createLearningBasedWB ( string  path_to_model )

static

Creates an instance of LearningBasedWB.

Parameters

path_to_model

Path to a .yml file with the model. If not specified, the default model is used

createLearningBasedWB() [2/2]

static LearningBasedWB OpenCVForUnity.XphotoModule.Xphoto.createLearningBasedWB ( )

static

Creates an instance of LearningBasedWB.

Parameters

path_to_model

Path to a .yml file with the model. If not specified, the default model is used

createSimpleWB()

static SimpleWB OpenCVForUnity.XphotoModule.Xphoto.createSimpleWB ( )

static

Creates an instance of SimpleWB.

createTonemapDurand() [1/6]

static TonemapDurand OpenCVForUnity.XphotoModule.Xphoto.createTonemapDurand ( float  gamma, float  contrast, float  saturation, float  sigma_color, float  sigma_space  )

static

Creates TonemapDurand object.

You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

Parameters

gamma	gamma value for gamma correction. See createTonemap
contrast	resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
saturation	saturation enhancement value. See createTonemapDrago
sigma_color	bilateral filter sigma in color space
sigma_space	bilateral filter sigma in coordinate space

createTonemapDurand() [2/6]

static TonemapDurand OpenCVForUnity.XphotoModule.Xphoto.createTonemapDurand ( float  gamma, float  contrast, float  saturation, float  sigma_color  )

static

Creates TonemapDurand object.

You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

Parameters

gamma	gamma value for gamma correction. See createTonemap
contrast	resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
saturation	saturation enhancement value. See createTonemapDrago
sigma_color	bilateral filter sigma in color space
sigma_space	bilateral filter sigma in coordinate space

createTonemapDurand() [3/6]

static TonemapDurand OpenCVForUnity.XphotoModule.Xphoto.createTonemapDurand ( float  gamma, float  contrast, float  saturation  )

static

Creates TonemapDurand object.

You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

Parameters

gamma	gamma value for gamma correction. See createTonemap
contrast	resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
saturation	saturation enhancement value. See createTonemapDrago
sigma_color	bilateral filter sigma in color space
sigma_space	bilateral filter sigma in coordinate space

createTonemapDurand() [4/6]

static TonemapDurand OpenCVForUnity.XphotoModule.Xphoto.createTonemapDurand ( float  gamma, float  contrast  )

static

Creates TonemapDurand object.

You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

Parameters

gamma	gamma value for gamma correction. See createTonemap
contrast	resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
saturation	saturation enhancement value. See createTonemapDrago
sigma_color	bilateral filter sigma in color space
sigma_space	bilateral filter sigma in coordinate space

createTonemapDurand() [5/6]

static TonemapDurand OpenCVForUnity.XphotoModule.Xphoto.createTonemapDurand ( float  gamma )

static

Creates TonemapDurand object.

You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

Parameters

gamma	gamma value for gamma correction. See createTonemap
contrast	resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
saturation	saturation enhancement value. See createTonemapDrago
sigma_color	bilateral filter sigma in color space
sigma_space	bilateral filter sigma in coordinate space

createTonemapDurand() [6/6]

static TonemapDurand OpenCVForUnity.XphotoModule.Xphoto.createTonemapDurand ( )

static

Creates TonemapDurand object.

You need to set the OPENCV_ENABLE_NONFREE option in cmake to use those. Use them at your own risk.

Parameters

gamma	gamma value for gamma correction. See createTonemap
contrast	resulting contrast on logarithmic scale, i. e. log(max / min), where max and min are maximum and minimum luminance values of the resulting image.
saturation	saturation enhancement value. See createTonemapDrago
sigma_color	bilateral filter sigma in color space
sigma_space	bilateral filter sigma in coordinate space

dctDenoising() [1/2]

static void OpenCVForUnity.XphotoModule.Xphoto.dctDenoising ( Mat  src, Mat  dst, double  sigma, int  psize  )

static

The function implements simple dct-based denoising.

<http://www.ipol.im/pub/art/2011/ys-dct/&gt;.

Parameters

src	source image
dst	destination image
sigma	expected noise standard deviation
psize	size of block side where dct is computed @sa fastNlMeansDenoising

dctDenoising() [2/2]

static void OpenCVForUnity.XphotoModule.Xphoto.dctDenoising ( Mat  src, Mat  dst, double  sigma  )

static

The function implements simple dct-based denoising.

<http://www.ipol.im/pub/art/2011/ys-dct/&gt;.

Parameters

src	source image
dst	destination image
sigma	expected noise standard deviation
psize	size of block side where dct is computed @sa fastNlMeansDenoising

inpaint()

static void OpenCVForUnity.XphotoModule.Xphoto.inpaint ( Mat  src, Mat  mask, Mat  dst, int  algorithmType  )

static

The function implements different single-image inpainting algorithms.

See the original papers [He2012] (Shiftmap) or [GenserPCS2018] and [SeilerTIP2015] (FSR) for details.

Parameters

src	source image INPAINT_SHIFTMAP: it could be of any type and any number of channels from 1 to 4. In case of 3- and 4-channels images the function expect them in CIELab colorspace or similar one, where first color component shows intensity, while second and third shows colors. Nonetheless you can try any colorspaces. INPAINT_FSR_BEST or INPAINT_FSR_FAST: 1-channel grayscale or 3-channel BGR image.
mask	mask (#CV_8UC1), where non-zero pixels indicate valid image area, while zero pixels indicate area to be inpainted
dst	destination image
algorithmType	see xphoto::InpaintTypes

oilPainting() [1/2]

static void OpenCVForUnity.XphotoModule.Xphoto.oilPainting ( Mat  src, Mat  dst, int  size, int  dynRatio, int  code  )

static

oilPainting See the book [Holzmann1988] for details.

Parameters

src	Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
dst	Output image of the same size and type as src.
size	neighbouring size is 2-size+1
dynRatio	image is divided by dynRatio before histogram processing
code	color space conversion code(see ColorConversionCodes). Histogram will used only first plane

oilPainting() [2/2]

static void OpenCVForUnity.XphotoModule.Xphoto.oilPainting ( Mat  src, Mat  dst, int  size, int  dynRatio  )

static

oilPainting See the book [Holzmann1988] for details.

Parameters

src	Input three-channel or one channel image (either CV_8UC3 or CV_8UC1)
dst	Output image of the same size and type as src.
size	neighbouring size is 2-size+1
dynRatio	image is divided by dynRatio before histogram processing

Member Data Documentation

BM3D_STEP1

const int OpenCVForUnity.XphotoModule.Xphoto.BM3D_STEP1 = 1

BM3D_STEP2

const int OpenCVForUnity.XphotoModule.Xphoto.BM3D_STEP2 = 2

BM3D_STEPALL

const int OpenCVForUnity.XphotoModule.Xphoto.BM3D_STEPALL = 0

HAAR

const int OpenCVForUnity.XphotoModule.Xphoto.HAAR = 0

INPAINT_FSR_BEST

const int OpenCVForUnity.XphotoModule.Xphoto.INPAINT_FSR_BEST = 1

INPAINT_FSR_FAST

const int OpenCVForUnity.XphotoModule.Xphoto.INPAINT_FSR_FAST = 2

INPAINT_SHIFTMAP

const int OpenCVForUnity.XphotoModule.Xphoto.INPAINT_SHIFTMAP = 0

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The documentation for this class was generated from the following file:

• OpenCVForUnity/Assets/OpenCVForUnity/org/opencv_contrib/xphoto/Xphoto.cs