Unpaired image denoising has achieved promising development over the last few years. Regardless of the performance, methods tend to heavily rely on underlying noise properties or any assumption which is not always practical. Alternatively, if we can ground the problem from a structural perspective rather than noise statistics, we can achieve a more robust solution. with such motivation, we propose a self-supervised denoising scheme that is unpaired and relies on spatial degradation followed by a regularized refinement. Our method shows considerable improvement over previous methods and exhibited consistent performance over different data domains.

翻译：过去几年来,未受重视的图像淡化取得了有希望的发展。不管表现如何,方法往往严重依赖潜在的噪音特性或并非始终切实可行的任何假设。 或者,如果我们能够从结构角度而不是噪音统计来解决问题,我们就能够实现一个更强有力的解决方案。有了这样的动机,我们建议一个不受监督的自我淡化计划,该计划将依赖空间退化,然后进行常规化的完善。我们的方法比以往的方法大有改进,在不同的数据领域表现了一贯的绩效。