报告题目：Tensor Factorization with Total Variation for Low-Rank Tensor Completion in Imaging Data
报告时间：2020年6月29日星期一8:30
报告方式：腾讯会议（ID：916 460 699）
报告内容：In this talk, Iwill focus on tensor factorization for low-rank tensor completion in imaging data.Due to the underlying redundancy of real-world imaging data, the low-tubal-rank tensor factorization (the tensor-tensor product of two factor tensors) can be used to approximate such tensor tensors very well.Motivated by the spatial/temporal smoothness of factor tensors in real-world imaging data, a low-tubal-rank tensor factorization model with a hybrid regularization combining total variation and Tikhonov regularization for low-rank tensor completion problem is developed.A proximal alternating minimization (PAM) algorithm is employed to tackle the corresponding minimization problem. A global convergence the PAM algorithm is established. Numerical results on color images, color videos, and multi-spectral images (MSIs)are reported to show the performance of the proposed method.
嘉宾简介：林学磊，香港浸会大学博士。2014年在宁夏大学获得信息与计算科学专业理学学士学位；2017年在澳门大学获得数学专业理学硕士学位；2017年至今在香港浸会大学数学系攻读博士学位。主要从事数值线性代数方面的研究，包括偏微分方程数值解、结构线性系统的快速迭代法、张量计算在数字图像处理方面的应用，已在J. Comput. Phys.，SIAM J. Matrix Anal. Appl.，J. Sci. Comput.，BIT. Numerical Mathematics，SIAM J. Numer. Anal.，Comput. Math. Appl.，J. Math. Imaging Vision等刊物以第一作者身份发表论文10余篇。曾在北京清华大学召开的“第八届世界华人数学家大会”上，获2019年“新世界数学奖”的优秀硕士论文银奖，是澳门首次获得该奖项，获“第十四届东亚工业与应用数学学会年会”优秀学生论文奖二等奖，获香港政府博士奖学金，获澳门研究生科技研发奖。
宁夏大学数学统计学院 宁夏师范学院数学与计算学院 联合承办
