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Journal of Electronic Imaging

Impulsive noise removal via sparse representation
Author(s): Fenge Chen; Guorui Ma; Liyu Lin; Qianqing Qin
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Paper 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 l [sub]1l 1 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.

Paper Details

Date Published: 12 November 2013
PDF: 12 pages
J. Electron. Imag. 22(4) 043014 doi: 10.1117/1.JEI.22.4.043014
Published in: Journal of Electronic Imaging Volume 22, Issue 4
Show Author Affiliations
Fenge Chen, Wuhan Univ. (China)
Guorui Ma, Wuhan Univ. (China)
Liyu Lin, Wuhan Univ. (China)
Qianqing Qin, Wuhan Univ. (China)

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