OpenCV for Unity
2.6.0
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.9.0/index.html ) for the details of the argument of the method.

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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> 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 dctbased denoising. More...  
static void  dctDenoising (Mat src, Mat dst, double sigma) 
The function implements simple dctbased denoising. More...  
static void  inpaint (Mat src, Mat mask, Mat dst, int algorithmType) 
The function implements different singleimage 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 fixedpoint 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 

static 
Implements an efficient fixedpoint approximation for applying channel gains, which is the last step of multiple white balance algorithms.
src  Input threechannel 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 

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.
Performs image denoising using the BlockMatching and 3Dfiltering algorithm <http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf> with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel 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 blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. 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.

static 
Creates an instance of GrayworldWB.

static 
Creates an instance of LearningBasedWB.
path_to_model  Path to a .yml file with the model. If not specified, the default model is used 

static 
Creates an instance of LearningBasedWB.
path_to_model  Path to a .yml file with the model. If not specified, the default model is used 

static 
Creates an instance of SimpleWB.

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

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

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

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

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

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

static 
The function implements simple dctbased denoising.
<http://www.ipol.im/pub/art/2011/ysdct/>.
src  source image 
dst  destination image 
sigma  expected noise standard deviation 
psize  size of block side where dct is computed @sa fastNlMeansDenoising 

static 
The function implements simple dctbased denoising.
<http://www.ipol.im/pub/art/2011/ysdct/>.
src  source image 
dst  destination image 
sigma  expected noise standard deviation 
psize  size of block side where dct is computed @sa fastNlMeansDenoising 

static 
The function implements different singleimage inpainting algorithms.
See the original papers [He2012] (Shiftmap) or [GenserPCS2018] and [SeilerTIP2015] (FSR) for details.
src  source image

mask  mask (#CV_8UC1), where nonzero pixels indicate valid image area, while zero pixels indicate area to be inpainted 
dst  destination image 
algorithmType  see xphoto::InpaintTypes 

static 
oilPainting See the book [Holzmann1988] for details.
src  Input threechannel 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 2size+1 
dynRatio  image is divided by dynRatio before histogram processing 
code  color space conversion code(see ColorConversionCodes). Histogram will used only first plane 

static 
oilPainting See the book [Holzmann1988] for details.
src  Input threechannel 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 2size+1 
dynRatio  image is divided by dynRatio before histogram processing 
const int OpenCVForUnity.XphotoModule.Xphoto.BM3D_STEP1 = 1 
const int OpenCVForUnity.XphotoModule.Xphoto.BM3D_STEP2 = 2 
const int OpenCVForUnity.XphotoModule.Xphoto.BM3D_STEPALL = 0 
const int OpenCVForUnity.XphotoModule.Xphoto.HAAR = 0 
const int OpenCVForUnity.XphotoModule.Xphoto.INPAINT_FSR_BEST = 1 
const int OpenCVForUnity.XphotoModule.Xphoto.INPAINT_FSR_FAST = 2 
const int OpenCVForUnity.XphotoModule.Xphoto.INPAINT_SHIFTMAP = 0 