Modification of fastNlMeansDenoising function for colored images sequences

dst = cv.fastNlMeansDenoisingMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize)
dst = cv.fastNlMeansDenoisingMulti(..., 'OptionName',optionValue, ...)

Input

• srcImgs Input 8-bit (or 16-bit with 'L1' norm) 1-channel, 2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
• imgToDenoiseIndex Target image to denoise index in srcImgs sequence (0-based index).
• temporalWindowSize Number of surrounding images to use for target image denoising. Should be odd. Images from imgToDenoiseIndex - temporalWindowSize/2 to imgToDenoiseIndex + temporalWindowSize/2 from srcImgs will be used to denoise srcImgs{imgToDenoiseIndex} image.

Output

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

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

Modification of cv.fastNlMeansDenoising function for images sequence where consequtive images have been captured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. For more details see: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394