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Multi-stage image denoising based on correlation coefficient matching and sparse dictionary pruning
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DOI：10.1016/j.sigpro.2011.06.014.png)
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摘要
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We present a novel image denoising method based on multiscale sparse representations. In tackling the conflicting problems of structure extraction and artifact suppression, we introduce a correlation coefficient matching criterion for sparse coding so as to extract more meaningful structures from the noisy image. On the other hand, we propose a dictionary pruning method to suppress noise. Based on the above techniques, an effective dictionary training method is developed. To further improve the denoising performance, we propose a multi-stage sparse coding framework where sparse representations are obtained in different scales to capture multiscale image features for effective denoising. The multi-stage coding scheme not only reduces the computational burden of previous multiscale denoising approaches, but more importantly, it also contributes to artifact suppression. Experimental results show that the proposed method achieves a state-of-the-art denoising performance in terms of both objective and subjective quality and provides significant improvements over other methods at high noise levels. (C) 2011 Elsevier B.V. All rights reserved.
Keyword:
Image denoising
Sparse representation
Correlation coefficient
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
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PATTERN RECOGNITION
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PATTERN RECOGNITIONIF7.6
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