Proximal Splitting Networks for Image Restoration

Understand

Image restoration problems are typically ill-posed requiring the design of suitable priors.

• These priors are typically hand-designed and are fully instantiated throughout the process.
• In this paper, we introduce a novel framework for handling inverse problems related to image restoration based on elements from the half quadratic splitting method and proximal operators.
• Modeling the proximal operator as a convolutional network, we defined an implicit prior on the image space as a function class during training.