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Proceedings Paper

Image super-resolution enhancement based on online learning and blind sparse decomposition
Author(s): Jinzheng Lu; Qiheng Zhang; Zhiyong Xu; Zhenming Peng
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Paper Abstract

This paper presents a different learning-based image super-resolution enhancement method based on blind sparse decomposition, in order to improve its resolution of a degraded one. Firstly, sparse decomposition based image super-resolution enhancement model is put forward according to the geometrical invariability of local image structures under different conditions of resolution. Secondly, for reducing the complexity of dictionary learning and enhancing adaptive representation ability of dictionary atoms, the over-complete dictionary is constructed using online learning fashion of the given low resolution image. Thirdly, since the fixed sparsity of the conventional matching pursuit algorithms for sparse decomposition can not fit all types of patches, the approach to sparse decomposition with blind sparsity can achieve relatively higher accurate sparse representation of an image patch. Lastly, atoms of high resolution dictionary and coefficients of representation of the given low-resolution image are synthesized to the desired SR image. Experimental results of the synthetic and real data demonstrate that the suggested framework can eliminate blurring degradation and annoying edge artifacts in the resulting images. The proposed method can be effectively applied to resolution enhancement of the single-frame low-resolution image.

Paper Details

Date Published: 2 December 2011
PDF: 8 pages
Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80040B (2 December 2011); doi: 10.1117/12.901540
Show Author Affiliations
Jinzheng Lu, Institute of Optics and Electronics (China)
Univ. of Electronic Science and Technology of China (China)
Graduate Univ. of the Chinese Academy of Sciences (China)
Qiheng Zhang, Institute of Optics and Electronics (China)
Zhiyong Xu, Institute of Optics and Electronics (China)
Zhenming Peng, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 8004:
MIPPR 2011: Pattern Recognition and Computer Vision
Jonathan Roberts; Jie Ma, Editor(s)

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