12 November 2013 Impulsive noise removal via sparse representation
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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 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.
© 2013 SPIE and IS&T
Fenge Chen, Fenge Chen, Guorui Ma, Guorui Ma, Liyu Lin, Liyu Lin, Qianqing Qin, Qianqing Qin, } "Impulsive noise removal via sparse representation," Journal of Electronic Imaging 22(4), 043014 (12 November 2013). https://doi.org/10.1117/1.JEI.22.4.043014 . Submission:
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