报 告 人：朱红 副教授，江苏大学

报告时间：11月20日（周五）08:00

报告地点：嘉定校区1-333

邀 请 人：马丽艳 副研究员

报告摘要：

In this talk, I will present two structured dictionary learning models to recover images corrupted by mixed Gaussian and impulse noise. These two models can be merged as lp-norm fidelity plus lq-norm regularization. The fidelity term is used to fit image patches and the regularization term is employed for sparse coding. Particularly, we utilize proximal (and proximal linearized) alternating minimization methods as the main solvers to deal with these two models. We remove the Gaussian noise under the assumption that the uncorrupted image can be approximated with a linear representation under an appropriate orthogonal basis. We use different ways to remove impulse noise for these two models.

报告人简介:

朱红，博士，江苏大学数学与应用数学系副教授，2016年于香港浸会大学数学系取得博士学位，主要研究方向非线性规划。 2015/01－2015/07访问新加坡国立大学储德林教授。2018/12－2019/12在加拿大西门莎菲大学做博士后，导师吕兆松教授。