Image denoising using Non-local Means Denoising algorithm

dst = cv.fastNlMeansDenoising(src)
dst = cv.fastNlMeansDenoising(src, 'OptionName',optionValue, ...)

Input

• src Input 8-bit (or 16-bit with L1 norm) 1-channel, 2-channel, 3-channel or 4-channel image.

Output

• dst Output image with the same size and type as src.

Options

• H Array of parameters regulating filter strength, either one parameter applied to all channels or one value per channel in dst. Big H value perfectly removes noise but also removes image details, smaller H value preserves details but also preserves some noise. default [3]
• TemplateWindowSize Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels. default 7
• SearchWindowSize Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater SearchWindowsSize - greater denoising time. Recommended value 21 pixels. default 21
• NormType Type of norm used for weight calculation. Can be either:
  • L2 (default)
  • L1

Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise.

This function expected to be applied to grayscale images. For colored images look at cv.fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in cv.fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different H parameter.