PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization

Understand

Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications.

• In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in imaging.
• Our regularizer, called patch normalizing flow regularizer (patchNR), involves a normalizing flow learned on small patches of very few images.
• In particular, the training is independent of the considered inverse problem such that the same regularizer can be applied for different forward operators acting on the same class of images.