星光直播 星光直播 报告[2025] 059号

(高水平大学建设系列报告1081号)

报告题目: Variational Bayesian Inference from Robust Tensor Analysis to Spatially-variant Deblurring

报告人：王超 助理教授（南方科技大学）

报告时间：2025年7月1日上午10:00-11:00

报告地点：校友广场307　　

报告摘要：Variational Bayesian inference offers a powerful framework for tackling key challenges in tensor analysis and image restoration, which are critical problems in machine learning and computer vision. This talk presents innovative approaches to address these challenges by integrating Bayesian principles, variational inference, and physical modeling. First, we introduce a Bayesian framework for Tensor Robust Principal Component Analysis (TRPCA) to recover low-rank structures and characterize sparse noise in mixed-noise scenarios. By embedding a low-rank tensor nuclear norm prior and a generalized sparsity-inducing prior within a Bayesian framework, our method learns the optimal tensor nuclear norm and balances low-rank and sparse components automatically. Second, we propose a novel physics-informed optimization framework for jointly solving depth estimation and image restoration from a single defocused image. By modeling the defocused image as a function of a depth map and an all-in-focus (AiF) image based on optical physics, the framework leverages their intrinsic connections. The depth map guides AiF image recovery, while the AiF image regularizes the depth map reconstruction via reconstruction error. A variational inference algorithm, parameterized with deep neural networks, ensures flexibility and high performance.

报告人简历： 王超，南方科技大学统计与数据科学系副研究员，博导，其研究方向主要为图像处理、科学计算与交叉学科的数据科学。在本领域期刊SIAM系列、IEEE汇刊等杂志及星光直播 会议发表星光直播 论文三十余篇。在2022年CVPR研讨会获得最佳论文，在2021年获深圳市鹏城孔雀计划特聘岗位，在2017年获得中国工业与应用数学学会年会最佳论文。主持国自然青年基金等3项，以课题负责人或骨干参与国家重点研发项目和香港研资局星光直播 基金项目。

欢迎师生参加！

邀请人：王江洲

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　　　　　　　　　　　　　　　　　　　　　 2025年6月26日