期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 22, 期 3, 页码 1108-1120出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2012.2227766
关键词
Image denoising; K-SVD; mixed noise; sparse representation; weighted norms
资金
- National Natural Science Foundation of China [11071023, 11201032]
- MOE (Ministry of Education) Tier II Project [T207N2202]
This paper proposes a general weighted l(2) - l(0) norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.
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