Image Denoising Models

Noise2Fast

Introduced by Lequyer et al. in Noise2Fast: Fast Self-Supervised Single Image Blind Denoising

Noise2Fast is a model for single image blind denoising. It is similar to masking based methods -- filling in the pixel gaps -- in that the network is blind to many of the input pixels during training. The method is inspired by Neighbor2Neighbor, where the neural network learns a mapping between adjacent pixels. Noise2Fast is tuned to speed by using a discrete four image training set obtained by a form of downsampling called “checkerboard downsampling.

Source: Noise2Fast: Fast Self-Supervised Single Image Blind Denoising

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Denoising 1 50.00%
Image Denoising 1 50.00%

Components


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories