Learning Image Processing with OpenCV by Gloria Bueno Garcia

Author:Gloria Bueno Garcia
Language: eng
Format: epub, pdf
Publisher: Packt Publishing

Note

More information about the nonlocal means and the TVL1 denoising algorithms can be found at http://www.ipol.im/pub/art/2011/bcm_nlm and http://znah.net/rof-and-tv-l1-denoising-with-primal-dual-algorithm.html, respectively.

OpenCV provides four functions to denoise color and grayscale images following the nonlocal means approach. For the TVL1 model, one function is provided. These functions are:

void fastNlMeansDenoising(InputArray src, OutputArray dst, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21): This denoises a single grayscale image loaded in src. The templateWindowSize and searchWindowSize parameters are the sizes in pixels of the template patch that is used to compute weights and the window that is used to compute the weighted average for the given pixel. These should be odd and their recommended values are 7 and 21 pixels, respectively. The h parameter regulates the effect of the algorithm. Larger h values remove more noise defects but with the drawback of removing more image details. The output is stored in dst.

void fastNlMeansDenoisingColored(InputArray src, OutputArray dst, float h = 3, float hForColorComponents = 3, int templateWindowSize = 7, int searchWindowSize = 21): This is a modification of the previous function for colored images. It converts the src image to the CIELAB color space and then separately denoises the L and AB components with the fastNlMeansDenoising function.

void fastNlMeansDenoisingMulti(InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, int templateWindowSize = 7, int searchWindowSize = 21): This uses an image sequence to obtain a denoised image. Two more parameters are needed in this case: imgToDenoiseIndex and temporalWindowSize. The value of imgToDenoiseIndex is the target image index in srcImgs to be denoised. Finally, temporalWindowSize is used to establish the number of surrounding images to be used for denoising. This should be odd.

void fastNlMeansDenoisingColoredMulti(InputArrayOfArrays srcImgs, OutputArray dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, float hForColorComponents = 3, int templateWindowSize = 7, int searchWindowSize = 21): This is based on the fastNlMeansDenoisingColored and fastNlMeansDenoisingMulti functions. The parameters are explained in the rest of the functions.

void denoise_TVL1(const std::vector<Mat>& observations, Mat& result, double lambda, int niters): This obtains a denoised image in result from one or more noisy images stored in observations. The lambda and niters parameters control the strength and the number of iterations of the algorithm.

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