报告题目：Robust AI-aided Imaging Models without Labeled Samples

报告人：包承龙

单位：清华大学

时间：2023年6月8日15:10-16:10

地点：河南大学(郑州校区)九章学堂南楼C座302#

ZOOM ID：567 306 5241

密码：123456

摘要：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. Finally, I will report the recent progresses on solving the preferred orientation problems in cyroEM using the developed tools.

简介：Chenglong Baois currently an assistant professor in Yau mathematical sciences center at Tsinghua university. He obtained the Ph.D. in Mathematics from National University of Singapore in 2014. His research interests include computational models and algorithms for solving imaging problems, and has published over 40 papers in top venues.