报 告 人：朱 炜 教授
报告题目: Image denoising using $L^p$-norm of mean curvature of image surface
报告时间：2019年5月21日（周二）下午4：00
报告地点：静远楼204学术报告厅
主办单位：数学与统计学院、科学技术研究院
报告人简介：
朱炜，教授，1994年毕业于清华大学数学系。2004年在北京大学师从张恭庆院士获得硕士学位。2004年在 University of California, Los Angeles师从美国 Tony F. Chan 院士获得博士学位。2004年-2008年在美国 Courant Institute of Mathematical Sciences 作博士后。2008年至今在美国ALABAMA大学工作。
报告摘要：
In this talk, I will discuss a new class of imaging denoising models by using the $L^p$-norm of mean curvature of image graphs as regularizers with 1<p<=2. The motivation of introducing such models is to add stronger regularizations than that of the original mean curvature based image denoising model in order to remove noise more efficiently. To minimize these variational models, we develop a novel augmented Lagrangian method, and one thus just needs to solve two linear elliptic equations to find saddle points of the associated augmented Lagrangian functionals. Specifically, we linearize the nonlinear term in one of the two subproblems and minimize a proximal-like functional that can be easily treated. We prove that the minimizer of the substitute functional does reduce the value of the original subproblem under certain conditions. Numerical results are presented to illustrate the features of the proposed models and also the efficiency of the designed algorithm.
联 系 人：常谦顺