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Statistically Adaptive Image Denoising Based on Overcomplete Topographic Sparse Coding

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Abstract

This paper presents a novel image denoising framework using overcomplete topographic model. To adapt to the statistics of natural images, we impose both spareseness and topograpgic constraints on the denoising model. Based on the overcomplete topographic model, our denoising system improves the previous work on the following aspects: multi-category based sparse coding, adaptive learning, local normalization, lasso shrinkage function, and subset selection. A large number of simulations have been performed to show the performance of the modified model, demonstrating that the proposed model achieves better denoising performance.

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Acknowledgments

The work of H. Zhao and L. Zhang was in part supported by the National Natural Science Foundation of China (Grant No. 61272251, 91120305).

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Authors and Affiliations

  1. Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science & Engineering, Shanghai Jiao Tong University, Shanghai, China

    Haohua Zhao, Zhiheng Huang & Liqing Zhang

  2. SONY Corporation, Tokyo, Japan

    Jun Luo, Takefumi Nagumo & Jun Murayama

Authors
  1. Haohua Zhao
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  2. Jun Luo
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  3. Zhiheng Huang
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  4. Takefumi Nagumo
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  5. Jun Murayama
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  6. Liqing Zhang
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Correspondence to Liqing Zhang.

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Zhao, H., Luo, J., Huang, Z. et al. Statistically Adaptive Image Denoising Based on Overcomplete Topographic Sparse Coding. Neural Process Lett 41, 357–369 (2015). https://doi.org/10.1007/s11063-014-9384-3

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  • DOI: https://doi.org/10.1007/s11063-014-9384-3

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