报告题目：From Compressed Sensing to Color Video Inpainting

报 告 人：贾志刚 教授

报告地点：格物楼3栋3306

报告时间：2024年10月4日 9: 00-10:00

报告摘要：The color video inpainting problem is one of the most challenging problem in the modern imaging science. It aims to recover a color video from a extremely low ratio of clean or noised pixels. However, there are less of models that can simultaneously preserve the coupling of color channels and the evolution of color video frames. In this academic presentation, we will introduce a new robust quaternion tensor completion (RQTC) model to solve this challenging problem. The main idea is to build a quaternion tensor optimization model to recover a low-rank quaternion tensor that represents the targeted color video and a sparse quaternion tensor that represents noise. To solve the case without low-rank property, we introduce a new low-rank learning RQTC model, which rearranges similar patches clustered by a quaternion learning method into sub-tensors satisfying the prior low-rank assumption. The exact recovery theory is derived for high-order RQTC and fast algorithms are also proposed with global convergence guarantees. We will start with compression sensing and end with looking ahead to AI methods for color video inpainting.

报告人简介：贾志刚，江苏师范大学教授。2023年入选江苏高校“青蓝工程”中青年学术带头人；2024年起担任 Numerical Algorithms 期刊编委。主要研究方向为数值代数与图像处理，至今已在IEEE Trans. Image Process.，SIAM J. Matrix Anal. Appl., SIAM J. Sci. Comput., SIAM J. Imaging Sci. 等期刊上发表学术论文40余篇，在科学出版社出版英文专著1部（独立作者），主持国家自然科学基金项目3项（青年1项、面上2项）和省高校自然科学研究重大项目1项，参加国家自然科学基金重大项目和国家重点研发计划课题各1项。2023 年荣获江苏省高等学校科学技术研究成果奖（自然科学奖）三等奖 (排名1/5)和第十届淮海科学技术奖（科技创新奖）一等奖(排名1/8)。曾先后到英国曼彻斯特大学、北京大学、香港浸会大学、澳门大学、复旦大学等高校数学系进行学术访问。