报告题目：Model-Aware and Data-Driven Learning Methods for Image Inverse Problems

报 告 人：张建平 教授（湘潭大学）

报告时间：2024年12月11日（星期三） 16:30-17:30

报告地点：数学楼115（大报告厅）

校内联系人： 宋晓良 副教授     联系方式：84708351-8304

报告摘要：Remote sensing images and medical imaging are essential for many applications, but their quality can usually be degraded due to limitations in imaging technology and complex imaging environments. To address this, various imaging methods have been developed to restore sharp and high-quality images from degraded observational data. However, most traditional model-based imaging methods usually require predefined hand-crafted prior assumptions, which are difficult to handle in complex applications. On the other hand, deep learning-based methods are often considered as black boxes, lacking transparency and interpretability. In this talk, we first present a new blind deblurring learning framework and high-order progressive SR network that utilizes alternating iterations of shrinkage thresholds. Then, we present a novel dual-domain unified framework that offers a great deal of flexibility for multi-sparse-view CT reconstruction through a single model. Experimental results on real and synthetic remote sensing image and medical image datasets demonstrate the superiority of our methods compared to existing methods.

报告人简介：张建平，湘潭大学数学与计算科学学院教授，博士生导师，于2012年获得新浦金350vip一博士学位，先后在在香港城市大学、利物浦大学从事博士后研究工作。长期致力于计算机视觉及图像处理中的数学问题、机器学习、深度学习及其应用方面的研究，相应成果发表在SIAM J. Imaging Sci.、SIAM J.Numer. Anal.、IEEE TMI、IEEE TGRS、IEEE JBHI、IEEE TCI、J Comput.Phys.等国际重要刊物上；主持完成国家自然科学基金2 项、湖南省科技厅及教育厅省部级项目4 项；作为主要骨干成员或子课题负责人参与科技部遥感重大项目等项目多项。