Image super-resolution reconstruction via RBM-based joint dictionary learning and sparse representation

Zhaohui Zhang; Anran Liu; Qian Lei

doi:10.1117/12.2214097

14 December 2015 Image super-resolution reconstruction via RBM-based joint dictionary learning and sparse representation

Zhaohui Zhang, Anran Liu, Qian Lei

Author Affiliations +

Proceedings Volume 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 981528 (2015) https://doi.org/10.1117/12.2214097
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

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.

Citation Download Citation

Zhaohui Zhang, Anran Liu, and Qian Lei "Image super-resolution reconstruction via RBM-based joint dictionary learning and sparse representation", Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 981528 (14 December 2015); https://doi.org/10.1117/12.2214097

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