Improved Self-Supervised Deep Image Denoising

Samuli Laine, Jaakko Lehtinen, Timo Aila

Keywords: denoising, self-supervised learning

TL;DR: We learn high-quality denoising using only single instances of corrupted images as training data.

Abstract: We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data. Inspired by a recent technique that removes the need for supervision through image pairs by employing networks with a "blind spot" in the receptive field, we address two of its shortcomings: inefficient training and poor final denoising performance. This is achieved through a novel blind-spot convolutional network architecture that allows efficient self-supervised training, as well as application of Bayesian distribution prediction on output colors. Together, they bring the self-supervised model on par with fully supervised deep learning techniques in terms of both quality and training speed in the case of i.i.d. Gaussian noise.