xiongchuanchao / neighbor2neighbor_pytorch

This is an unofficial implementation for the paper "NEIGHBOR2NEIGHBOR: SELF-SUPERVISED DENOISING FROM SINGLE NOISY IMAGES"

Neighbor2Neighbor_Pytorch

This is an unofficial implementation for the paper "NEIGHBOR2NEIGHBOR: SELF-SUPERVISED DENOISING FROM SINGLE NOISY IMAGES"

Introduction

This is an implementation for the paper "NEIGHBOR2NEIGHBOR"https://arxiv.org/abs/2101.02824, and i have tied this method for real noise removal task in my own dataset, which has presented some effects to some degree. But i haven't applied it to some traditional denoising tasks, e.g., Gaussian nosie and Posson noise removing. You could try it easily in this codes.

Basic requirements

1. Pytorch > 1.3.0
2. Nvidia apex ( this codes are easily used for multi-gpus training)
3. Some Python packages

Training and testing

1. First, you need to add the data preprocessing file based your tasks.
2. Update the config.yaml file
3. training and testing with python main.py

Extra discussions

This is an extended work for Noise2Noise https://arxiv.org/abs/1803.04189, which all depend on the powerful zero-mean noise prior hypothesis.