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Image Deblurring Via Total Variation Based Structured Sparse Model Selection

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Abstract

In this paper, we study the image deblurring problem based on sparse representation over learned dictionary which leads to promising performance in image restoration in recent years. However, the commonly used overcomplete dictionary is not well structured. This shortcoming makes the approximation be unstable and demand much computational time. To overcome this, the structured sparse model selection (SSMS) over a family of learned orthogonal bases was proposed recently. In this paper, We further analyze the properties of SSMS and propose a model for deblurring under Gaussian noise. Numerical experimental results show that the proposed algorithm achieves competitive performance. As a generalization, we give a modified model for deblurring under salt-and-pepper noise. The resulting algorithm also has a good performance.

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Author information

Authors and Affiliations

  1. Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, China

    Liyan Ma

  2. Jiangsu Research and Development Center for Internet of Things, Wuxi, 214135, China

    Liyan Ma

  3. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Tieyong Zeng

  4. HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, 518057, China

    Tieyong Zeng

Authors
  1. Liyan Ma
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  2. Tieyong Zeng
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Corresponding author

Correspondence to Tieyong Zeng.

Additional information

The research is supported by the National Natural Science Foundation of China (No. 11271049, 61402462), RGC 211911, 12302714 and RFGs of HKBU.

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Ma, L., Zeng, T. Image Deblurring Via Total Variation Based Structured Sparse Model Selection. J Sci Comput 67, 1–19 (2016). https://doi.org/10.1007/s10915-015-0067-7

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  • DOI: https://doi.org/10.1007/s10915-015-0067-7

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