Image super-resolution by dictionary concatenation and sparse representation with approximate L0 norm minimization

Authors:
Jinzheng Lu;Qiheng Zhang;Zhiyong Xu;Zhenming Peng
Affiliations:
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China and School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu ...;Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China;Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China;School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China
Venue:
Computers and Electrical Engineering
Year:
2012

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Abstract

This paper proposes a different image super-resolution (SR) reconstruction scheme, based on the newly advanced results of sparse representation and the recently presented SR methods via this model. Firstly, we online learn a subsidiary dictionary with the degradation estimation of the given low-resolution image, and concatenate it with main one offline learned from many natural images with high quality. This strategy can strengthen the expressive ability of dictionary atoms. Secondly, the conventional matching pursuit algorithms commonly use a fixed sparsity threshold for sparse decomposition of all image patches, which is not optimal and even introduces annoying artifacts. Alternatively, we employ the approximate L"0 norm minimization to decompose accurately the patch over its dictionary. Thus the coefficients of representation with variant number of nonzero items can exactly weight atoms for those complicated local structures of image. Experimental results show that the proposed method produces high-resolution images that are competitive or superior in quality to results generated by similar techniques.