Abstract：
Conventional image denoising algorithms attempt to remove noise on the basis of spectral separation between signal and noise. However, in practice, the spectra of signal and noise usually overlap. As a result, conventional denoising algorithms often suppress noises at the cost of losing details in fine texture and causing blurring in output images. This paper formulates the image denoising problem by adopting a new model of sparse and low-rank matrix decomposition. Based on this model, the alternating direction method (ADM) is utilized to obtain the restored image. Our experiment demonstrates that the ADM suppresses salt-and-pepper noise more efficiently and better preserves detail information in the input image, as compared to the commonly used median filtering method.

ReleaseDate：2014-07-21 17:04:34

Funds：National Natural Science Foundation of China (61102097, 61102096), Science and Technology Support Program of Tianjin (11ZCGHHZ00700), and Natural Science Foundation of Tianjin (11JCYBJC06900)

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