Noise2Fast

2000년 도입 · 논문 2편에서 사용

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.

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

소개 논문: Noise2Fast: Fast Self-Supervised Single Image Blind Denoising