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
this https URL

本文提出了一种新的自监督去噪框架 Noise2Same，不需要 J - 不变性或额外的噪声模型信息，可在更广泛的去噪应用中使用，实验证明 Noise2Same 在去噪性能和训练效率方面显著优于以往的自监督去噪方法。