Reviews: High-Quality Self-Supervised Deep Image Denoising

Pros: -The Bayesian analysis with different noise models is interesting. The ablation study is carefully done and confirms the importance of the central pixel integration at test time. This is an important result and may be used in future works. I also find interesting that performance is not too degraded when noise level is unknown. It suggests the potential for image denoising using only single instances of corrupted images as training data.