Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising

• URL: http://arxiv.org/abs/2010.11971v1
• Date: Thu, 22 Oct 2020 18:12:26 GMT
• Title: Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
• Authors: Yaochen Xie, Zhengyang Wang, Shuiwang Ji
• Abstract summary: We introduce Noise2Same, a novel self-supervised denoising framework. In particular, Noise2Same requires neither J-invariance nor extra information about the noise model. Our results show that our Noise2Same remarkably outperforms previous self-supervised denoising methods.
• Score: 54.730707387866076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised frameworks that learn denoising models with merely individual noisy images have shown strong capability and promising performance in various image denoising tasks. Existing self-supervised denoising frameworks are mostly built upon the same theoretical foundation, where the denoising models are required to be J-invariant. However, our analyses indicate that the current theory and the J-invariance may lead to denoising models with reduced performance. In this work, we introduce Noise2Same, a novel self-supervised denoising framework. In Noise2Same, a new self-supervised loss is proposed by deriving a self-supervised upper bound of the typical supervised loss. In particular, Noise2Same requires neither J-invariance nor extra information about the noise model and can be used in a wider range of denoising applications. We analyze our proposed Noise2Same both theoretically and experimentally. The experimental results show that our Noise2Same remarkably outperforms previous self-supervised denoising methods in terms of denoising performance and training efficiency. Our code is available at https://github.com/divelab/Noise2Same.

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