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Hybrid sparse-representation-based approach to image super-resolution reconstruction

[+] Author Affiliations
Di Zhang

Guangdong Medical University, School of Information Engineering, Dongguan, China

Jiazhong He

Shaoguan University, Department of Physics, Shaoguan, China

J. Electron. Imaging. 26(2), 023008 (Mar 21, 2017). doi:10.1117/1.JEI.26.2.023008
History: Received August 19, 2016; Accepted March 1, 2017
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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.

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Citation

Di Zhang and Jiazhong He
"Hybrid sparse-representation-based approach to image super-resolution reconstruction", J. Electron. Imaging. 26(2), 023008 (Mar 21, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.2.023008


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