题目：Low-rank Regularization based Models for Image Restoration

内容简介：In this talk, we develop several low-rank matrix/tensor regularization based models for image restoration. Firstly, we proposed a novel nonlocal low-rank method for efficient multiplicative noise removal and develop a proximal alternating reweighted minimization (PARM) algorithm to solve the optimization problem resulting from the model. Secondly, magnetic resonance (MR) images are frequently corrupted by Rician noise during image acquisition and transmission. We propose a new non-local low-rank regularization (NLLRR) method including an optimization model and an efficient iterative algorithm to remove Rician noise. Thirdly, we tackle the problem of having mixed additive Gaussian white noise and impulse noise. We propose to remove this mixed noise through a nonlocal low-rank regularized two-phase (NLR-TP) approach. Fourthly, we develop a new method based on low-rank tensor regularization approximation for hyperspectral image recovery, called non-convex low-rank tensor approximation (NLRTA). The method utilizes a low-rank prior of the tensor formed by spatial and spectral information, while exploring the intrinsic structure of HSI from noise observations.

报告人：鲁坚

报告人简介：现任深圳大学数学科学学院特聘教授、博士生导师、副院长，深圳市数学学会理事长，深圳国家应用数学中心（深圳大学）常务副主任，广东省计算数学学会副理事长，中国数学会理事，深圳市现代机器学习与应用重点实验室主任；主持国家自然科学基金联合基金重点项目1项、面上项目2项，联合主持国家基金数学天元基金“数学与智能+”交叉重点专项1项。

时  间：2025年6月8日（周日）下午20:00开始

地  点：腾讯会议608-994-505

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2025年6月5日