Fixed readme
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95
README.md
95
README.md
@ -133,65 +133,78 @@ All the filters used and their parameters are described below.
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## Available filters with parameters
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-median blur
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-ksize - Kernel size (int)
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- median blur
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-gaussian blur
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-sigma - Gaussian kernel standart deviation (int)
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- ksize - Kernel size (int)
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-bilateral blur
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-diameter - Diameter of pixel neighborhood used for filtering (int)
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-sigmaColor - Standard deviation for grayvalue/color distance (int)
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-sigmaSpace - Standard deviation for range distance in pixels (int)
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- gaussian blur
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-bilateral_scikit
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-sigmaColor - Standard deviation for grayvalue/color distance (float)
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-sigmaSpace - Standard deviation for range distance in pixels (float)
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- sigma - Gaussian kernel standart deviation (int)
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-nlmeans (non-local means)
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-patch_size - Size of patches used for denoising (int)
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-patch_distance - Distance in pixels where to search for patches (int)
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-h - Cut-off distance, higher means more smoothed image (float)
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- bilateral blur
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-total_variation
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-weight - Denoising weight. (float)
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- diameter - Diameter of pixel neighborhood used for filtering (int)
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- sigmaColor - Standard deviation for grayvalue/color distance (int)
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- sigmaSpace - Standard deviation for range distance in pixels (int)
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-block_match
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-sigma - ? (?)
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- bilateral_scikit
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-unsharp mask scikit
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-radius - Radius of the gaussian filter (int)
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-amount - Strength of the unsharp mask (float)
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- sigmaColor - Standard deviation for grayvalue/color distance (float)
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- sigmaSpace - Standard deviation for range distance in pixels (float)
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- nlmeans (non-local means)
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- patch_size - Size of patches used for denoising (int)
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- patch_distance - Distance in pixels where to search for patches (int)
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- h - Cut-off distance, higher means more smoothed image (float)
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- total_variation
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- weight - Denoising weight. (float)
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- block_match
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- sigma - ? (?)
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- unsharp mask scikit
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- radius - Radius of the gaussian filter (int)
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- amount - Strength of the unsharp mask (float)
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-farid
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- farid
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-meijering
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- meijering
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-sato
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- sato
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-hessian
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-sigmas - ? (float)
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- hessian
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-invert
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- sigmas - ? (float)
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-scale_values
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- invert
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-binarize
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-threshold - value to cut differentiate pixels (int)
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-maxval - maximal value (int) ??
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-type - ? (str)
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- scale_values
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-binarize_otsu
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- binarize
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-add_margin
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-margin - number of pixels to add to the sides of the image (int)
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-color - color value of newly added pixels (int)
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- threshold - value to cut differentiate pixels (int)
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- maxval - maximal value (int) ??
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- type - ? (str)
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-erode
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-kernel - kernel shape (numpy.matrix)
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- binarize_otsu
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-dilate
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-kernel - kernel shape (numpy.matrix)
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- add_margin
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- margin - number of pixels to add to the sides of the image (int)
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- color - color value of newly added pixels (int)
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- erode
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- kernel - kernel shape (numpy.matrix)
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- dilate
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- kernel - kernel shape (numpy.matrix)
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# Comparison
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