报 告 人： Zhu wei Assistant Professor
Department of Mathematics,
报告题目：A mean curvature based image denoising model and its fast algorithm
报告时间：
报告地点：静远楼1508学术报告厅
主办单位：太阳成集团、科技处
报告摘要：High order derivative has been widely employed in developing variational models in image processing to accomplish more advanced tasks. In this talk, we will discuss a variational model for image denoising using a second order regularizer, that is, the L1 norm of the mean curvature of the image surface for a given image. Besides eliminating noise and preserving edges of objects, the reguaralizer helps keep sharp corners and image contrasts and also remove the staircase effect. However, due to the high order of the regularizer as well as its nondifferentiabilty and nonlinearity, it is nontrivial to develop efficient algorithms to minimize the proposed functional. In this talk, we will also discuss a fast algorithm using the augmented Lagrangian method for the associated minimization problem. We will present numerical experiments to show the properties of the image denoising model, to compare it with some classic models such as the well-known Rudin-Osher-Fatemi model and Euler’s elastica based models, and validate the efficiency of the proposed fast algorithm.
Zhu wei Assistant Professor简介：
美国塔斯卡卢萨阿拉巴马大学数学系，终身助理教授。1994年在中国清华大学数学系获理学学士学位，1999年在中国北京大学数学系获理学硕士学位，2004年在美国加州大学洛杉矶分校数学系获理学博士学位。2004年9月-2008年8月在纽约大学数学科学研究生读博士后，师从Michael J. Shelley教授和Robert M. Shapley教授。 2008年至今在塔斯卡卢萨阿拉巴马大学数学系任助理教授，主要研究领域是图像处理和计算机图形学，计算神经科学，偏微分方程和变分方法等。