Xphoto Class Reference


                    
                    
                    +createGrayworldWB

Creates an instance of GrayworldWB

Declaration

Objective-C

+ (nonnull GrayworldWB *)createGrayworldWB;

Swift

class func createGrayworldWB() -> GrayworldWB


                    
                    
                    +createLearningBasedWB:

Creates an instance of LearningBasedWB

Declaration

Objective-C

+ (nonnull LearningBasedWB *)createLearningBasedWB:
    (nonnull NSString *)path_to_model;

Swift

class func createLearningBasedWB(path_to_model: String) -> LearningBasedWB

Parameters


                                path_to_model

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


                    
                    
                    +createLearningBasedWB

Creates an instance of LearningBasedWB

Declaration

Objective-C

+ (nonnull LearningBasedWB *)createLearningBasedWB;

Swift

class func createLearningBasedWB() -> LearningBasedWB


                    
                    
                    +createSimpleWB

Creates an instance of SimpleWB

Declaration

Objective-C

+ (nonnull SimpleWB *)createSimpleWB;

Swift

class func createSimpleWB() -> SimpleWB


                    
                    
                    +createTonemapDurand:contrast:saturation:sigma_color:sigma_space:

Creates TonemapDurand object

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

Declaration

Objective-C

+ (nonnull TonemapDurand *)createTonemapDurand:(float)gamma
                                      contrast:(float)contrast
                                    saturation:(float)saturation
                                   sigma_color:(float)sigma_color
                                   sigma_space:(float)sigma_space;

Swift

class func createTonemapDurand(gamma: Float, contrast: Float, saturation: Float, sigma_color: Float, sigma_space: Float) -> TonemapDurand

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:contrast:saturation:sigma_color:

Creates TonemapDurand object

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

Declaration

Objective-C

+ (nonnull TonemapDurand *)createTonemapDurand:(float)gamma
                                      contrast:(float)contrast
                                    saturation:(float)saturation
                                   sigma_color:(float)sigma_color;

Swift

class func createTonemapDurand(gamma: Float, contrast: Float, saturation: Float, sigma_color: Float) -> TonemapDurand

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


                    
                    
                    +createTonemapDurand:contrast:saturation:

Creates TonemapDurand object

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

Declaration

Objective-C

+ (nonnull TonemapDurand *)createTonemapDurand:(float)gamma
                                      contrast:(float)contrast
                                    saturation:(float)saturation;

Swift

class func createTonemapDurand(gamma: Float, contrast: Float, saturation: Float) -> TonemapDurand

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


                    
                    
                    +createTonemapDurand:contrast:

Creates TonemapDurand object

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

Declaration

Objective-C

+ (nonnull TonemapDurand *)createTonemapDurand:(float)gamma
                                      contrast:(float)contrast;

Swift

class func createTonemapDurand(gamma: Float, contrast: Float) -> TonemapDurand

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.


                    
                    
                    +createTonemapDurand:

Creates TonemapDurand object

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

Declaration

Objective-C

+ (nonnull TonemapDurand *)createTonemapDurand:(float)gamma;

Swift

class func createTonemapDurand(gamma: Float) -> TonemapDurand

Parameters


                                gamma

gamma value for gamma correction. See createTonemap are maximum and minimum luminance values of the resulting image.


                    
                    
                    +createTonemapDurand

Creates TonemapDurand object

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

are maximum and minimum luminance values of the resulting image.

Declaration

Objective-C

+ (nonnull TonemapDurand *)createTonemapDurand;

Swift

class func createTonemapDurand() -> TonemapDurand


                    
                    
                    +applyChannelGains:dst:gainB:gainG:gainR:

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

Declaration

Objective-C

+ (void)applyChannelGains:(nonnull Mat *)src
                      dst:(nonnull Mat *)dst
                    gainB:(float)gainB
                    gainG:(float)gainG
                    gainR:(float)gainR;

Swift

class func applyChannelGains(src: Mat, dst: Mat, gainB: Float, gainG: Float, gainR: Float)

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:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:step: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta
              normType:(int)normType
                  step:(int)step
         transformType:(int)transformType;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float, normType: Int32, step: Int32, transformType: Int32)

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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta
              normType:(int)normType
                  step:(int)step;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float, normType: Int32, step: Int32)

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. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta
              normType:(int)normType;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float, normType: Int32)

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. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float)

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. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32)

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. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32)

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. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize:searchWindowSize:blockMatchingStep1: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32)

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. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize:searchWindowSize: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32)

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. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h:templateWindowSize: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32)

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. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst:h: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
                   dst:(nonnull Mat *)dst
                     h:(float)h
    templateWindowSize:(int)templateWindowSize;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float, templateWindowSize: Int32)

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. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dst: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src dst:(nonnull Mat *)dst h:(float)h;

Swift

class func bm3dDenoising(src: Mat, dst: Mat, h: Float)

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. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src dst:(nonnull Mat *)dst;

Swift

class func bm3dDenoising(src: Mat, dst: Mat)

Parameters

src

Input 8-bit or 16-bit 1-channel image.

dst

Output image with the same size and type as src. removes image details, smaller h value preserves details but also preserves some noise. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 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.

 @sa
 fastNlMeansDenoising


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType:step: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta
              normType:(int)normType
                  step:(int)step
         transformType:(int)transformType;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float, normType: Int32, step: Int32, transformType: Int32)

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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta:normType: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta
              normType:(int)normType
                  step:(int)step;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float, normType: Int32, step: Int32)

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. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep:beta: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta
              normType:(int)normType;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float, normType: Int32)

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. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize:slidingStep: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep
                  beta:(float)beta;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32, beta: Float)

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. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2:groupSize: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize
           slidingStep:(int)slidingStep;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32, slidingStep: Int32)

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. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1:blockMatchingStep2: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2
             groupSize:(int)groupSize;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32, groupSize: Int32)

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. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize:blockMatchingStep1: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1
    blockMatchingStep2:(int)blockMatchingStep2;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32, blockMatchingStep2: Int32)

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. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize:searchWindowSize: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize
    blockMatchingStep1:(int)blockMatchingStep1;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32, blockMatchingStep1: Int32)

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. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h:templateWindowSize: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize
      searchWindowSize:(int)searchWindowSize;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32, searchWindowSize: Int32)

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. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2:h: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
              dstStep1:(nonnull Mat *)dstStep1
              dstStep2:(nonnull Mat *)dstStep2
                     h:(float)h
    templateWindowSize:(int)templateWindowSize;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float, templateWindowSize: Int32)

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. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1:dstStep2: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
             dstStep1:(nonnull Mat *)dstStep1
             dstStep2:(nonnull Mat *)dstStep2
                    h:(float)h;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat, h: Float)

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. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +bm3dDenoising:dstStep1: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.

Declaration

Objective-C

+ (void)bm3dDenoising:(nonnull Mat *)src
             dstStep1:(nonnull Mat *)dstStep1
             dstStep2:(nonnull Mat *)dstStep2;

Swift

class func bm3dDenoising(src: Mat, dstStep1: Mat, dstStep2: Mat)

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. removes image details, smaller h value preserves details but also preserves some noise. Should be power of 2. Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. but yields more accurate results. 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. @sa fastNlMeansDenoising`


                    
                    
                    +dctDenoising:dst:sigma:psize:

The function implements simple dct-based denoising

 <http://www.ipol.im/pub/art/2011/ys-dct/>.

Declaration

Objective-C

+ (void)dctDenoising:(nonnull Mat *)src
                 dst:(nonnull Mat *)dst
               sigma:(double)sigma
               psize:(int)psize;

Swift

class func dctDenoising(src: Mat, dst: Mat, sigma: Double, psize: Int32)

Parameters

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


                    
                    
                    +dctDenoising:dst:sigma:

The function implements simple dct-based denoising

 <http://www.ipol.im/pub/art/2011/ys-dct/>.

Declaration

Objective-C

+ (void)dctDenoising:(nonnull Mat *)src
                 dst:(nonnull Mat *)dst
               sigma:(double)sigma;

Swift

class func dctDenoising(src: Mat, dst: Mat, sigma: Double)

Parameters

`src`	source image
`dst`	destination image
`sigma`	expected noise standard deviation @sa fastNlMeansDenoising


                    
                    
                    +inpaint:mask:dst:algorithmType:

The function implements different single-image inpainting algorithms.

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

Declaration

Objective-C

+ (void)inpaint:(nonnull Mat *)src
             mask:(nonnull Mat *)mask
              dst:(nonnull Mat *)dst
    algorithmType:(int)algorithmType;

Swift

class func inpaint(src: Mat, mask: Mat, dst: Mat, algorithmType: Int32)

Parameters

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


                    
                    
                    +oilPainting:dst:size:dynRatio:code:

oilPainting See the book CITE: Holzmann1988 for details.

Declaration

Objective-C

+ (void)oilPainting:(nonnull Mat *)src
                dst:(nonnull Mat *)dst
               size:(int)size
           dynRatio:(int)dynRatio
               code:(int)code;

Swift

class func oilPainting(src: Mat, dst: Mat, size: Int32, dynRatio: Int32, code: Int32)

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:dst:size:dynRatio:

oilPainting See the book CITE: Holzmann1988 for details.

Declaration

Objective-C

+ (void)oilPainting:(nonnull Mat *)src
                dst:(nonnull Mat *)dst
               size:(int)size
           dynRatio:(int)dynRatio;

Swift

class func oilPainting(src: Mat, dst: Mat, size: Int32, dynRatio: Int32)

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

Xphoto

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