12月21日:Zeng Tieyong
发布时间:2017-12-20  阅读次数:963
报告题目:Convex and Non-Convex Optimization in Image Recovery and Segmentation 
报告人: Prof. Zeng Tieyong
(Department of Mathematics, The Chinese University of Hong Kong & Hong Kong Baptist University)
主持人:方发明
报告时间:2017年12月21日周四 16:00—17:00
报告地点:中北校区理科大楼B楼816会议室
 
报告摘要:
    In this talk, we present some recent progress on variational approaches for image recovery and segmentation. First, a new convex variational model for restoring images degraded by blur and Rician noise is proposed. Based on the mathematical property of the noise, a quadratic penalty function technique is utilized to obtain a strictly convex model under mild condition, which ensures the uniqueness of the solution and the stabilization of the algorithm. Numerical results are presented to demonstrate the good performance of our approach. The idea of convex relaxation is then extended to other image recovery and segmentation tasks.  Finally, we also discuss the image recovery issue in the framework of dictionary learning if time permitted.
 
报告人简介:
    曾铁勇,博士,香港中文大学&香港浸会大学副教授,于2000年本科毕业于北京大学,2007年巴黎第十三大学获得博士学位。主要研究领域包括优化理论,图像处理,反问题等。在优化、图像处理、反问题的国际一流杂志SIAM Journal on Imaging Sciences, SIAM Journal on Scientific Computing, International Journal of Computer Vision, Journal of Scientific Computing,IEEE Transactions on Image Processing,Pattern Recognition,Journal of Mathematical Imaging and Vision等发表过多篇SCI论文。
华东师范大学计算机科学与软件工程学院
www.sei.ecnu.edu.cn Copyright School of Computer Science and Software Engineering
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