报告题目：Continuous Modeling Perspective for Imaging Science 
报告人：赵熙乐教授 
报告时间：2025年5月28日（星期三）15:00-16:00 
报告地点：数学科学学院205 
摘要：To tackle inverse problems in imaging science, the regularizer, serving as an indispensable cornerstone in modeling, are usually introduced. In this talk, we will begin by reviewing the classical regularizers, including local regularizers, nonlocal regularizers, and global regularizers. We then will discuss the limitations of classical hand-crafted regularizers (e.g., expressive capability, applicability, and flexibility). To address the above limitations of classical regularizers, we suggest a unified Continuous Modeling Perspective for imaging science, which continuously represents discrete data by elegantly leveraging tiny neural networks. This paradigm allows us to readily deconstruct and reconstruct the classical regularizers, thus unleashing the potential of regularizers. Extensive experiments demonstrate the promising performance of the continuous modeling perspective. 

报告人简介：电子科技大学教授、博导，入选四川省学术和技术带头人、四川省天府青城计划。第一/通讯在权威期刊SIAM系列(SISC和SIIMS)和IEEE系列(TPAMI、TIP、TSP和TNNLS等)及顶会CVPR等发表研究工作。研究成果获四川省自然科学一等奖、四川省科技进步一等奖、计算数学会青年优秀论文竞赛二等奖。主持国自然面上项目、四川省杰出青年科学基金项目、华为项目。

编辑：王苗   审核：蒋毅   终审：屈加文