报告题目：Structured Dictionary Learning for Image Denoising under Mixed Gaussian and Impulse Noise

主 讲 人：朱 红

单 位：江苏大学

时 间：11月25日16:00

腾 讯 ID：948 791 387

摘 要:

Although image denoising as a basic task of image restoration has been widely studied in the past decades, there are not many studies on mixed noise denoising. We propose two structured dictionary learning models to recover images corrupted by mixed Gaussian and impulse noise, which can be merged aslp-norm fidelity pluslq-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.

简 介：

朱红，江苏大学副教授，硕士生导师。2016年毕业于香港浸会大学，获得哲学博士。2012年毕业于WilliamHill中文官方网站，获理学硕士。2018年12月到2019年12月受国家留学基金委的资助在加拿大西蒙弗雷泽大学访问。主要从事非线性最优化理论及其相关领域的研究。主持国家自然科学基金1项，江苏省青年基金1项。