Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images
Kwanyoung Kim, Jong Chul Ye

TL;DR
Noise2Score introduces a unified self-supervised image denoising framework based on Tweedie's formula, capable of handling various noise types without clean images, and significantly outperforms existing methods in benchmark tests.
Contribution
The paper presents a novel method that unifies existing self-supervised denoising approaches using Tweedie's formula and score function estimation, enabling versatile noise removal.
Findings
Outperforms state-of-the-art methods on benchmark datasets
Works effectively with Gaussian, Poisson, and Gamma noises
Uses a single network for multiple noise types
Abstract
Recently, there has been extensive research interest in training deep networks to denoise images without clean reference. However, the representative approaches such as Noise2Noise, Noise2Void, Stein's unbiased risk estimator (SURE), etc. seem to differ from one another and it is difficult to find the coherent mathematical structure. To address this, here we present a novel approach, called Noise2Score, which reveals a missing link in order to unite these seemingly different approaches. Specifically, we show that image denoising problems without clean images can be addressed by finding the mode of the posterior distribution and that the Tweedie's formula offers an explicit solution through the score function (i.e. the gradient of log likelihood). Our method then uses the recent finding that the score function can be stably estimated from the noisy images using the amortized residual…
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Taxonomy
TopicsImage and Signal Denoising Methods · Seismic Imaging and Inversion Techniques · Cell Image Analysis Techniques
