An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration without Clean Data"

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoisi…

Deep CNN for learning image restoration without clean data!

pytorch implementation of noise2noise for Cryo-EM image denoising

Noise2Noise: Learning Image Restoration without Clean Data

Just another noise 2 noise implementation (JANNI) with Keras

Noise2Noise implementation

Noise2Noise is an AI denoiser trained with noisy images only. We implemented a ligther version which trains faster on smaller pictures without losing performance and an even simpler one where every low-level component was implemented from scratch, including a reimplementation of autograd.

An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration without Clean Data" (AI can now fix your grainy photos by only looking at grainy photos. Noise2Noise)

The standard approach to image reconstruction using deep learning is to use clean image priors for training purposes. In this project, we attempt to achieve denoising without using a clean image prior and yet, achieving a performance comparable to, or sometimes, even better than that obtained using the conventional approach.