Denoising

• KNLMeansCL

video = core.knlm.KNLMeansCL(video, d, a, s, h, channels="YUV")

• d = Set the number of past and future frame that the filter uses for denoising the current frame. d=0 uses 1 frame, while d=1 uses 3 frames and so on. Usually, larger it the better the result of the denoising.
• a = Set the radius of the search window. a=0 uses 1 pixel, while a=1 uses 9 pixels and so on. Usually, larger it the better the result of the denoising.
• s = Set the radius of the similarity neighbourhood window. The impact on performance is low, therefore it depends on the nature of the noise.
• h = Controls the strength of the filtering. Larger values will remove more noise.

Official Wiki

• BM3D

import mvsfunc as mvs # mvsfunc script required
video = mvs.BM3D(video, sigma, radius1, profile1, matrix)

• sigma = The strength of filtering.
• radius1 = Temporal radius (0 - Spatial Denoising / 1~16 - Spatial-Temporal Denoising)
• profile1 = Preset profile (fast, lc, np, high, very high)
• matrix = color matrix of input clip.

• dfftestMC

import muvsfunc as muf # muvsfunc script required
video = muf.dfttestMC(video, sigma, mdg=True)

• sigma = The strength of filtering.
• mdg = Run MDeGrain before dfttest (True/False)