OpenCVForUnity.XphotoModule.Xphoto Class Reference
Static Public Member Functions | |
| 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. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| 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> with several computational optimizations. Noise expected to be a gaussian white noise. | |
| static GrayworldWB | createGrayworldWB () |
| Creates an instance of GrayworldWB. | |
| static LearningBasedWB | createLearningBasedWB () |
| Creates an instance of LearningBasedWB. | |
| static LearningBasedWB | createLearningBasedWB (string path_to_model) |
| Creates an instance of LearningBasedWB. | |
| static SimpleWB | createSimpleWB () |
| Creates an instance of SimpleWB. | |
| static TonemapDurand | createTonemapDurand () |
| Creates TonemapDurand object. | |
| static TonemapDurand | createTonemapDurand (float gamma) |
| Creates TonemapDurand object. | |
| static TonemapDurand | createTonemapDurand (float gamma, float contrast) |
| Creates TonemapDurand object. | |
| static TonemapDurand | createTonemapDurand (float gamma, float contrast, float saturation) |
| Creates TonemapDurand object. | |
| static TonemapDurand | createTonemapDurand (float gamma, float contrast, float saturation, float sigma_color) |
| Creates TonemapDurand object. | |
| static TonemapDurand | createTonemapDurand (float gamma, float contrast, float saturation, float sigma_color, float sigma_space) |
| Creates TonemapDurand object. | |
| static void | dctDenoising (Mat src, Mat dst, double sigma) |
| The function implements simple dct-based denoising. | |
| static void | dctDenoising (Mat src, Mat dst, double sigma, int psize) |
| The function implements simple dct-based denoising. | |
| static void | inpaint (Mat src, Mat mask, Mat dst, int algorithmType) |
| The function implements different single-image inpainting algorithms. | |
| static void | oilPainting (Mat src, Mat dst, int size, int dynRatio) |
| oilPainting See the book [Holzmann1988] for details. | |
| static void | oilPainting (Mat src, Mat dst, int size, int dynRatio, int code) |
| oilPainting See the book [Holzmann1988] for details. | |
Static Public Attributes | |
| const int | BM3D_STEP1 = 1 |
| const int | BM3D_STEP2 = 2 |
| const int | BM3D_STEPALL = 0 |
| const int | HAAR = 0 |
| const int | INPAINT_FSR_BEST = 1 |
| const int | INPAINT_FSR_FAST = 2 |
| const int | INPAINT_SHIFTMAP = 0 |
Member Function Documentation
◆ applyChannelGains()
|
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]
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [2/24]
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [3/24]
|
static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [4/24]
|
static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [5/24]
|
static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [6/24]
|
static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [7/24]
|
static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [8/24]
|
static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [9/24]
|
static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [10/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [11/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [12/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [13/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [14/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [15/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [16/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [17/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [18/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [19/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [20/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [21/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [22/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [23/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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() [24/24]
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static |
Performs image denoising using the Block-Matching and 3D-filtering algorithm <http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf> 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
◆ createGrayworldWB()
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Creates an instance of GrayworldWB.
◆ createLearningBasedWB() [1/2]
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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]
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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()
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Creates an instance of SimpleWB.
◆ createTonemapDurand() [1/6]
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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]
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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]
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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]
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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]
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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]
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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]
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The function implements simple dct-based denoising.
<http://www.ipol.im/pub/art/2011/ys-dct/>.
- Parameters
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src source image dst destination image sigma expected noise standard deviation psize size of block side where dct is computed
- See also
- fastNlMeansDenoising
◆ dctDenoising() [2/2]
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The function implements simple dct-based denoising.
<http://www.ipol.im/pub/art/2011/ys-dct/>.
- Parameters
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src source image dst destination image sigma expected noise standard deviation psize size of block side where dct is computed
- See also
- fastNlMeansDenoising
◆ inpaint()
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The function implements different single-image inpainting algorithms.
See the original papers [He2012] (Shiftmap) or [GenserPCS2018] and [SeilerTIP2015] (FSR) for details.
- Parameters
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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]
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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
◆ oilPainting() [2/2]
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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
Member Data Documentation
◆ BM3D_STEP1
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◆ BM3D_STEP2
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◆ BM3D_STEPALL
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◆ HAAR
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◆ INPAINT_FSR_BEST
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◆ INPAINT_FSR_FAST
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◆ INPAINT_SHIFTMAP
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The documentation for this class was generated from the following file:
- OpenCVForUnity/Assets/OpenCVForUnity/org/opencv_contrib/xphoto/Xphoto.cs
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