Infrared Image De-noising Based On K-SVD Over-complete Dictionaries Learning

被引：0

作者：

Shan, Bin ^{[1
]}

Hao, Wei ^{[1
]}

Zhao, Rui

机构：

[1] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China

来源：

2012 5TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING (CISP) | 2012年

关键词：

over-complete dictionaries; image de-noising; infrared noise;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The sparse representation of image based on overcomplete dictionaries is a new image representation theory. Using the redundancy of over-complete dictionaries can effectively capture the various structure detail characteristics of an image, so as to realize the efficient representation of the image. In this paper we propose an infrared image de-noising algorithm based on K-SVD over-complete dictionaries learning using the overcomplete dictionary image sparse representation theory. The experimental results compared with the common de-noising algorithm processing results prove the effectiveness of the proposed method.

引用

页码：316 / 320

页数：5

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