Adversarial Regularizers in Inverse Problems

Understand

Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods.

• Among those variational regularization models are one of the most popular approaches.
• We propose a new framework for applying data-driven approaches to inverse problems, using a neural network as a regularization functional.
• The network learns to discriminate between the distribution of ground truth images and the distribution of unregularized reconstructions.