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Proceedings Paper

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene
Author(s): Wei Wu; Dewei Zhao; Huan Zhang
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Paper Abstract

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

Paper Details

Date Published: 14 December 2015
PDF: 7 pages
Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 981515 (14 December 2015); doi: 10.1117/12.2204775
Show Author Affiliations
Wei Wu, Wuhan Univ. of Technology (China)
Dewei Zhao, Wuhan Univ. of Technology (China)
Huan Zhang, Wuhan Univ. of Technology (China)

Published in SPIE Proceedings Vol. 9815:
MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Jianguo Liu; Hong Sun, Editor(s)

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