SINGLE IMAGE SUPER RESOLUTION WITH HIGH RESOLUTION DICTIONARY

被引：0

作者：

Mu, Guangwu ^{[1
]}

Gao, Xinbo ^{[1
]}

Zhang, Kaibing ^{[1
]}

Li, Xuelong ^{[2
]}

Tao, Dacheng ^{[2
]}

机构：

[1] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China

[2] Chinese Acad Sci, Inst Opt & Precis Mech, OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China

来源：

2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2011年

基金：

中国国家自然科学基金;

关键词：

Dynamic group sparsity; non-local means; sparse representation; super resolution;

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：

Image super resolution (SR) is a technique to estimate or synthesize a high resolution (HR) image from one or several low resolution (LR) images. This paper proposes a novel framework for single image super resolution based on sparse representation with high resolution dictionary. Unlike the previous methods, the training set is constructed from the HR images instead of HR-LR image pairs. Due to this property, there is no need to retrain a new dictionary when the zooming factor changed. Given a testing LR image, the patch-based representation coefficients and the desired image are estimated alternately through the use of dynamic group sparsity, the fidelity term and the non-local means regularization. Experimental results demonstrate the effectiveness of the proposed algorithm.

引用

页码：1141 / 1144

页数：4

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