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Proceedings Paper

Image super-resolution reconstruction via RBM-based joint dictionary learning and sparse representation
Author(s): Zhaohui Zhang; Anran Liu; Qian Lei
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Paper Abstract

In this paper, we propose a method for single image super-resolution(SR). Given the training set produced from large amount of high-low resolution image patches, an over-complete joint dictionary is firstly learned from a pair of high-low resolution image feature space based on Restricted Boltzmann Machines (RBM). Then for each low resolution image patch densely extracted from an up-scaled low resolution input image , its high resolution image patch can be reconstructed based on sparse representation. Finally, the reconstructed image patches are overlapped to form a large image, and a high resolution image can be achieved by means of iterated residual image compensation. Experimental results verify the effectiveness of the proposed method.

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, 981528 (14 December 2015); doi: 10.1117/12.2214097
Show Author Affiliations
Zhaohui Zhang, Hebei Normal Univ. (China)
Key Lab. of Computational Mathematics with Applications (China)
Anran Liu, Hebei Normal Univ. (China)
Qian Lei, Hebei Normal Univ. (China)
Key Lab. of Computational Mathematics with Applications (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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