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学术报告一百零一:Rank Minimization with Applications to Image Noise Removal

来源:本站 作者: 时间:2018-12-12 9次

数学与统计学院学术报告[2018] 101号

(高水平大学建设系列报告220号)

报告题目: Rank Minimization with Applications to Image Noise Removal

报告人:

文有为  教授(湖南师范大学)

报告时间:2018年12月5日下午4:00—5:00

报告地点:

科技楼501室

报告内容:Rank minimization problem has a wide range of applications in different areas. However, since this problem is NP-hard  and non-convex, the frequently used method is to  replace the matrix rank minimization with  nuclear norm minimization. Nuclear norm is the convex envelope of the matrix rank and it is more computationally tractable. Matrix completion is a special case of rank minimization problem. In this paper,  we consider directly using matrix rank as the regularization term instead of nuclear norm in the cost function for matrix completion problem.  The solution  is analyzed and obtained by a hard-thresholding operation on the singular values of the observed matrix. Then by exploiting patch-based nonlocal self-similarity scheme, we apply the proposed rank minimization algorithm to remove white Gaussian additive noise in images. Gamma multiplicative noise is also removed  in logarithm domain. The experimental results illustrate  that the proposed algorithm can remove noises in images more efficiently than nuclear norm can do. And the results are also competitive with those obtained by using the existing state-of-the-art  noise removal methods in the literature.

报告人简历:文有为,博士,教授,博士生导师。2003至2006年在香港大学数学系攻读博士学位。2008年至2011年分别在新加坡国立大学淡马锡实验室、香港中文大学数学系从事博士后研究。曾多次访问德国、意大利、新加坡、香港等国家和地区的知名大学。主要从事科学计算、数字图像处理领域的研究。在SIAM J. Sci. Comput.、SIAM J. Imaging

Sciences、SIAM Multiscale Model. Simul.、SIAM J. Matrix Anal. Appl.、Numer. Linear

Algebra Appl.、IEEE Trans. Image

Process.等期刊发表论文20余篇。先后主持教育部留学回国基金1项、国家自然科学基金2项,参与香港研究资助局基金2项。

数学与统计学院

2018年12月5日

深圳大学数学与统计学院