Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 26, Issue 7, Pages 3171-3186Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2017.2676466
Keywords
Mixed noise removal; low-rank; Laplacian scale mixture; alternative minimization
Funding
- Major State Basic Research Development Program of China (973 Program) [2013CB329402]
- Natural Science Foundation of China [61622210, 61471281, 61472301, 61632019, 61390512]
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Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse noise is a challenging problem due to its difficulties in an accurate modeling of the distributions of the mixture noise. Many efforts have been made to first detect the locations of the impulse noise and then recover the clean image with image in painting techniques from an incomplete image corrupted by AWGN. However, it is quite challenging to accurately detect the locations of the impulse noise when the mixture noise is strong. In this paper, we propose an effective mixture noise removal method based on Laplacian scale mixture (LSM) modeling and nonlocal low-rank regularization. The impulse noise is modeled with LSM distributions, and both the hidden scale parameters and the impulse noise are jointly estimated to adaptively characterize the real noise. To exploit the nonlocal self-similarity and low-rank nature of natural image, a nonlocal low-rank regularization is adopted to regularize the denoising process. Experimental results on synthetic noisy images show that the proposed method outperforms existing mixture noise removal methods.
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