We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a blind spot in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d.

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.

We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a "blind spot" in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data.