Compressed Sensing with Deep Image Prior and Learned Regularization

Understand

We propose a novel method for compressed sensing recovery using untrained deep generative models.

• Our method is based on the recently proposed Deep Image Prior (DIP), wherein the convolutional weights of the network are optimized to match the observed measurements.
• We show that this approach can be applied to solve any differentiable linear inverse problem, outperforming previous unlearned methods.
• Unlike various learned approaches based on generative models, our method does not require pre-training over large datasets.