Learning Robust Imaging Models without Paired Data

44th Imaging & Inverse Problems (IMAGINE) OneWorld SIAM-IS Virtual Seminar Series Talk
Date: Wednesday, May 25, 10:00am Eastern
Speaker: Prof. Chenglong Bao, Tsinghua University

Abstract : The observations in practical imaging systems always contain complex noise such that classical approaches are difficult to obtain
satisfactory results. In recent years, deep neural networks directly learned a map between the noisy and clean images based on the training on paired data. Despite its promising results in various tasks, collecting
the training data is difficult and time-consuming in practice. In this talk, in the unpaired data regime, we will discuss our recent progress for building AI-aided robust models and their applications in image
processing. Leveraging the Bayesian inference framework, our model combines classical mathematical modeling and deep neural networks to improve interpretability. Experimental results on various real datasets
validate the advantages of the proposed methods.