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Impulsive noise removal via sparse representation

[+] Author Affiliations
Fenge Chen

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

Guorui Ma

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

Liyu Lin

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

Qianqing Qin

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

J. Electron. Imaging. 22(4), 043014 (Nov 12, 2013). doi:10.1117/1.JEI.22.4.043014
History: Received July 11, 2013; Revised October 10, 2013; Accepted October 16, 2013
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Abstract.  We propose a two-phase approach to restore images corrupted by impulsive noise based on sparse representation. In the first phase, we identify the outlier candidates—the pixels that are likely to be corrupted by impulsive noise. In the second phase, the image is denoised via dictionary learning by using the outlier-free data. The dictionary learning task is formulated as a modified l1l1 minimization objective and solved under the alternating direction method. The experimental results demonstrate that our method can obtain better performances in terms of both quantitative evaluation and visual quality than the state-of-the-art impulse denoising methods.

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Citation

Fenge Chen ; Guorui Ma ; Liyu Lin and Qianqing Qin
"Impulsive noise removal via sparse representation", J. Electron. Imaging. 22(4), 043014 (Nov 12, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.4.043014


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