Abstract

Many interesting computational imaging problems can be formulated as imaging inverse problems. Since these problems are often ill-posed, one needs to integrate all the available prior knowledge for obtaining high-quality solutions. This talk focuses on the class of methods based on using “image restoration” deep neural network as data-driven implicit priors on images. The roots of the methods discussed in this talk can be traced to the popular Plug-and-Play Priors (PnP) family of methods for solving inverse problems. The talk will present applications of learned implicit priors for image generation in limited angle computed tomography, recovery of continuously represented microscopy images, and solving blind inverse problems in magnetic resonance imaging.

Abstact

TBA