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