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Non-local sparse regularization model with application to image denoising

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Abstract

We study problems related to denoising of natural images corrupted by Gaussian white noise. Important structures in natural images such as edges and textures are jointly characterized by local variation and nonlocal invariance. Both provide valuable schemes in the regularization of image denoising. In this paper, we propose a framework to explore two sets of ideas involving on the one hand, locally learning a dictionary and estimating the sparse regularization signal descriptions for each coefficient; and on the other hand, nonlocally enforcing the invariance constraint by introducing patch self-similarities of natural images into the cost functional. The minimization of this new cost functional leads to an iterative thresholding-based image denoising algorithm; its efficient implementation is discussed. Experimental results from image denoising tasks of synthetic and real noisy images show that the proposed method outperforms the state-of-the-art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61370138, 61271435, 61103130, U1301251), National Program on Key Basic Research Projects (973 programs) (Grant Nos. 2010CB731804-1, 2011CB706901-4), Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality (Grant Nos. IDHT20130513, CIT&TCD20130513), Beijing Municipal Natural Science Foundation (Grant No. 4141003), and Beijing Municipal Party Committee Organization Department of Outstanding Talent Project (Grant No. 2010D005022000011).

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Authors and Affiliations

  1. Beijing Key Laboratory of Information Service Engineering, College of Information Technology, Beijing Union University, Beijing, 100101, China

    Ning He, Jin-Bao Wang, Lu-Lu Zhang & Guang-Mei Xu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Ke Lu

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  1. Ning He
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  2. Jin-Bao Wang
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  3. Lu-Lu Zhang
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  4. Guang-Mei Xu
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  5. Ke Lu
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Correspondence to Ning He.

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He, N., Wang, JB., Zhang, LL. et al. Non-local sparse regularization model with application to image denoising. Multimed Tools Appl 75, 2579–2594 (2016). https://doi.org/10.1007/s11042-015-2471-2

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  • DOI: https://doi.org/10.1007/s11042-015-2471-2

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