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学术沙龙系列报告(第八期):Robust Color Video Inpainting Based on Quaternion Tensor Representation

2022-10-13    点击:[]

报告题目:Robust Color Video Inpainting Based on Quaternion Tensor Representation

报 告 人:贾志刚 教授

报告地点:腾讯会议315409366

报告时间:2022年10月21日星期五9: 00-10:00

报告摘要:The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image and has been widely applied in many recently proposed machining learning algorithms for image processing. However, there is no theoretical analysis on its working principle in the literature. In this talk, we discover a potential causality between NSS and low-rank property of color images, which is also available to grey images. A new patch group based NSS prior scheme is proposed to learn explicit NSS models of natural color images. The numerical low-rank property of patched matrices is also rigorously proved. The NSS-based QMC algorithm computes an optimal low-rank approximation to the high-rank color image, resulting in high PSNR and SSIM measures and particularly the better visual quality. A new tensor NSS-based QMC method is also presented to solve the color video inpainting problem based on quaternion tensor representation. The numerical experiments on color images and videos indicate the advantages of NSS-based QMC over the state-of-the-art methods.

报告人简介:贾志刚,江苏师范大学教授、硕士生导师。2009年毕业于华东师范大学数学系,获理学博士学位。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., J. Sci. Comput., Numer. Linear Algebra Appl.等国际知名期刊上发表学术论文40余篇,在科学出版社出版专著和译著各1部,主持国家自然科学基金项目3项、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1项。先后入选江苏师范大学“第一批高层次人才队伍后备人选”、“三育人先进个人”、“校先进工作者”等。曾先后到英国曼彻斯特大学、香港浸会大学、澳门大学等高校数学系进行学术访问。现兼职为中国高等教育学会教育数学专业委员会常务理事、江苏省计算数学学会理事、美国Math Review评论员等,同时为SIMAX,SISC, SSIMS, Inverse Problem,Automatic,JCAM, IEEE TSP,Signal Processing等学术期刊的审稿人。