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Journal of Electronic Imaging

Hybrid sparse-representation-based approach to image super-resolution reconstruction
Author(s): Di Zhang; Jiazhong He
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Paper Abstract

This paper presents a hybrid sparse-representation-based approach to single-image super-resolution reconstruction. Our main contribution is threefold: (1) jointly utilize nonlocal similarity of intensity image and low-rank property of gradient image under the framework of sparse representation; (2) incorporate both the high-resolution (HR) and low-resolution dictionaries into the reconstruction process; and (3) incorporate both the unknown HR image and the sparse coefficients into a single objective function. By alternatively minimizing the objective function with respect to the unknown HR image and the sparse coefficients, we get an estimate of the target HR image. Extensive experiments validate that compared with many state-of-the-art algorithms the proposed method yields comparable results for noiseless images and achieves much better results for noisy images.

Paper Details

Date Published: 21 March 2017
PDF: 9 pages
J. Electron. Imag. 26(2) 023008 doi: 10.1117/1.JEI.26.2.023008
Published in: Journal of Electronic Imaging Volume 26, Issue 2
Show Author Affiliations
Di Zhang, Guangdong Medical College (China)
Jiazhong He, Shaoguan Univ. (China)

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