【报告人】金其余教授

【报告题目】Edge adaptive hybrid regularization model for image deblurring

【报告摘要】

（1）Parameter selection is crucial to regularization-based image restoration methods. Generally speaking, a spatially fixed parameter for the regularization term does not perform well for both edge and smooth areas. A larger parameter for the regularization term reduces noise better in smooth areas but blurs edge regions, while a small parameter sharpens edge but causes residual noise.

（2）In this talk, an automated spatially adaptive regularization model, which combines the harmonic and TV terms, is proposed for the image reconstruction from noisy and blurred observation. The proposed model detects the edges and then spatially adjusts the parameters of Tikhonov and TV regularization terms for each pixel according to the edge information. Accordingly, the edge information matrix will also be dynamically updated during the iterations. Computationally, the newly-established model is convex, which can be solved by the semi-proximal alternating direction method of multipliers (sPADMM) with a linear convergence rate. Numerical simulation results demonstrate that the proposed model effectively preserves the image edges and eliminates the noise and blur at the same time.

【报告时间】2022年7月1日，上午9：00——12：00

【报告形式】腾讯会议；会议号：991-504-065

【报告人简介】金其余，内蒙古大学教授、博导。法国南布列塔尼大学应用数学博士，巴黎六大、上海交通大学博士后，巴黎-萨克雷高等师范学校访问学者，内蒙古自治区“青年科技英才支持计划”青年科技领军人才。长期与国内外多所大学保持合作，包括法国巴黎-萨克雷高等师范学校、巴黎六大、Centre Inria Rennes等。研究领域包括：图像处理、计算机视觉与最优化。相应成果发表于SIAM Journal on Imaging Sciences、Cell子刊Structure、Inverse Problems、Journal of scientific computing、Journal of Mathematical Imaging and Vision等期刊。主持国家自然科学基金、内蒙古自然科学基金等项目多项。