报告题目：A Quick Trip on Matrix/Tensor Decomposition Methods for High-Dimensional Image Recovery

主 讲 人：赵熙乐

单 位：电子科技大学

时 间：9月24日19:00

腾 讯 ID：523 635 943

摘 要：

Recently, low-rank tensor decomposition methods have received increasing attention for high-dimensional data recovery. However, only considering the low-rank structure of high-dimensional data is not sufficient for high-dimensional data recovery, especially for extremely complex imaging scenarios. In this talk, we will discuss how to bring into play the respective strengths of self-supervised learning and matrix/tensor decomposition for high-dimensional data recovery. Extensive numerical examples including inpainting, denoising, and snapshot compressed sensing are delivered to demonstrate the superiority of the suggested methods over state-of-the-art methods.

简 介：

赵熙乐，电子科技大学教授(博导)，入选电子科技大学百人计划和四川省学术和技术带头人后备人选。主要研究兴趣为高维图像反问题的数学和深度方法等，受邀撰写Elsevier出版的英文书籍一章，在科学出版社合作出版专著1部，以第一或通讯作者在应用数学和图像处理权威期刊发表学术论文50余篇(入选ESI高被引文章5篇)，包括SIAM J. Imaging Sci.、SIAM J. Sci. Comput.、J. Sci. Comput、IEEE Trans. Image Process.、IEEE Trans. Neural Netw. Learn. Syst.、IEEE Trans. Cybernetics、IEEE Trans. Geosci. Remote Sens.及AAAI等；主持国家自然科学基金面上项目和青年项目、四川省应用基础研究项目等。研究成果获四川省科技进步一等奖(自然科学类)，四川省科技进步一等奖(科技进步类)，中国计算数学学会青年优秀论文竞赛二等奖、首届川渝科技学术大会优秀论文一等奖、首届四川省数学会应用数学奖二等奖。