Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery

Understand

Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors.

• Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination.
• To cope with these challenges, we design a cascaded network architecture that unrolls the proximal gradient iterations by permeating benefits from generative residual networks (ResNet) to modeling the proximal operator.
• A mixture of pixel-wise and perceptual costs is then deployed to train proximals.