OpenCV
3.2.0
Open Source Computer Vision

Classes  
class  cv::xphoto::GrayworldWB 
Grayworld white balance algorithm. More...  
class  cv::xphoto::LearningBasedWB 
More sophisticated learningbased automatic white balance algorithm. More...  
class  cv::xphoto::SimpleWB 
A simple white balance algorithm that works by independently stretching each of the input image channels to the specified range. For increased robustness it ignores the top and bottom \(p\%\) of pixel values. More...  
class  cv::xphoto::WhiteBalancer 
The base class for auto white balance algorithms. More...  
Enumerations  
enum  cv::xphoto::Bm3dSteps { cv::xphoto::BM3D_STEPALL = 0, cv::xphoto::BM3D_STEP1 = 1, cv::xphoto::BM3D_STEP2 = 2 } 
BM3D algorithm steps. More...  
enum  cv::xphoto::InpaintTypes { cv::xphoto::INPAINT_SHIFTMAP = 0 } 
various inpainting algorithms More...  
enum  cv::xphoto::TransformTypes { cv::xphoto::HAAR = 0 } 
BM3D transform types. More...  
Functions  
void  cv::xphoto::applyChannelGains (InputArray src, OutputArray dst, float gainB, float gainG, float gainR) 
Implements an efficient fixedpoint approximation for applying channel gains, which is the last step of multiple white balance algorithms. More...  
void  cv::xphoto::bm3dDenoising (InputArray src, InputOutputArray dstStep1, OutputArray dstStep2, float h=1, int templateWindowSize=4, int searchWindowSize=16, int blockMatchingStep1=2500, int blockMatchingStep2=400, int groupSize=8, int slidingStep=1, float beta=2.0f, int normType=cv::NORM_L2, int step=cv::xphoto::BM3D_STEPALL, int transformType=cv::xphoto::HAAR) 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise. More...  
void  cv::xphoto::bm3dDenoising (InputArray src, OutputArray dst, float h=1, int templateWindowSize=4, int searchWindowSize=16, int blockMatchingStep1=2500, int blockMatchingStep2=400, int groupSize=8, int slidingStep=1, float beta=2.0f, int normType=cv::NORM_L2, int step=cv::xphoto::BM3D_STEPALL, int transformType=cv::xphoto::HAAR) 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise. More...  
Ptr< GrayworldWB >  cv::xphoto::createGrayworldWB () 
Creates an instance of GrayworldWB. More...  
Ptr< LearningBasedWB >  cv::xphoto::createLearningBasedWB (const String &path_to_model=String()) 
Creates an instance of LearningBasedWB. More...  
Ptr< SimpleWB >  cv::xphoto::createSimpleWB () 
Creates an instance of SimpleWB. More...  
void  cv::xphoto::dctDenoising (const Mat &src, Mat &dst, const double sigma, const int psize=16) 
The function implements simple dctbased denoising. More...  
void  cv::xphoto::inpaint (const Mat &src, const Mat &mask, Mat &dst, const int algorithmType) 
The function implements different singleimage inpainting algorithms. More...  
void cv::xphoto::applyChannelGains  (  InputArray  src, 
OutputArray  dst,  
float  gainB,  
float  gainG,  
float  gainR  
) 
Implements an efficient fixedpoint approximation for applying channel gains, which is the last step of multiple white balance algorithms.
src  Input threechannel image in the BGR color space (either CV_8UC3 or CV_16UC3) 
dst  Output image of the same size and type as src. 
gainB  gain for the B channel 
gainG  gain for the G channel 
gainR  gain for the R channel 
void cv::xphoto::bm3dDenoising  (  InputArray  src, 
InputOutputArray  dstStep1,  
OutputArray  dstStep2,  
float  h = 1 , 

int  templateWindowSize = 4 , 

int  searchWindowSize = 16 , 

int  blockMatchingStep1 = 2500 , 

int  blockMatchingStep2 = 400 , 

int  groupSize = 8 , 

int  slidingStep = 1 , 

float  beta = 2.0f , 

int  normType = cv::NORM_L2 , 

int  step = cv::xphoto::BM3D_STEPALL , 

int  transformType = cv::xphoto::HAAR 

) 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel image. 
dstStep1  Output image of the first step of BM3D with the same size and type as src. 
dstStep2  Output image of the second step of BM3D with the same size and type as src. 
h  Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. 
templateWindowSize  Size in pixels of the template patch that is used for blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. Affect performance linearly: greater searchWindowsSize  greater denoising time. Must be larger than templateWindowSize. 
blockMatchingStep1  Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. 
blockMatchingStep2  Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. 
groupSize  Maximum size of the 3D group for collaborative filtering. 
slidingStep  Sliding step to process every next reference block. 
beta  Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. 
normType  Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results. 
step  Step of BM3D to be executed. Possible variants are: step 1, step 2, both steps. 
transformType  Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported. 
This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.
void cv::xphoto::bm3dDenoising  (  InputArray  src, 
OutputArray  dst,  
float  h = 1 , 

int  templateWindowSize = 4 , 

int  searchWindowSize = 16 , 

int  blockMatchingStep1 = 2500 , 

int  blockMatchingStep2 = 400 , 

int  groupSize = 8 , 

int  slidingStep = 1 , 

float  beta = 2.0f , 

int  normType = cv::NORM_L2 , 

int  step = cv::xphoto::BM3D_STEPALL , 

int  transformType = cv::xphoto::HAAR 

) 
Performs image denoising using the BlockMatching and 3Dfiltering algorithm http://www.cs.tut.fi/~foi/GCFBM3D/BM3D_TIP_2007.pdf with several computational optimizations. Noise expected to be a gaussian white noise.
src  Input 8bit or 16bit 1channel image. 
dst  Output image with the same size and type as src. 
h  Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise. 
templateWindowSize  Size in pixels of the template patch that is used for blockmatching. Should be power of 2. 
searchWindowSize  Size in pixels of the window that is used to perform blockmatching. Affect performance linearly: greater searchWindowsSize  greater denoising time. Must be larger than templateWindowSize. 
blockMatchingStep1  Block matching threshold for the first step of BM3D (hard thresholding), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. 
blockMatchingStep2  Block matching threshold for the second step of BM3D (Wiener filtering), i.e. maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. 
groupSize  Maximum size of the 3D group for collaborative filtering. 
slidingStep  Sliding step to process every next reference block. 
beta  Kaiser window parameter that affects the sidelobe attenuation of the transform of the window. Kaiser window is used in order to reduce border effects. To prevent usage of the window, set beta to zero. 
normType  Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results. 
step  Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL. BM3D_STEP2 is not allowed as it requires basic estimate to be present. 
transformType  Type of the orthogonal transform used in collaborative filtering step. Currently only Haar transform is supported. 
This function expected to be applied to grayscale images. Advanced usage of this function can be manual denoising of colored image in different colorspaces.
Ptr<GrayworldWB> cv::xphoto::createGrayworldWB  (  ) 
Creates an instance of GrayworldWB.
Ptr<LearningBasedWB> cv::xphoto::createLearningBasedWB  (  const String &  path_to_model = String()  ) 
Creates an instance of LearningBasedWB.
path_to_model  Path to a .yml file with the model. If not specified, the default model is used 
void cv::xphoto::dctDenoising  (  const Mat &  src, 
Mat &  dst,  
const double  sigma,  
const int  psize = 16 

) 
The function implements simple dctbased denoising.
http://www.ipol.im/pub/art/2011/ysdct/.
src  source image 
dst  destination image 
sigma  expected noise standard deviation 
psize  size of block side where dct is computed 
The function implements different singleimage inpainting algorithms.
See the original paper [68] for details.
src  source image, it could be of any type and any number of channels from 1 to 4. In case of 3 and 4channels 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. 
mask  mask (CV_8UC1), where nonzero pixels indicate valid image area, while zero pixels indicate area to be inpainted 
dst  destination image 
algorithmType  see xphoto::InpaintTypes 