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Non-Cooperative Target Tracking, Fusion and Control pp 163–185Cite as

Constrained Image Deblurring with Sparse Proximal Newton Splitting Method

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Part of the book series: Information Fusion and Data Science ((IFDS))

Abstract

This chapter proposes a framework of sparse proximal Newton splitting method for constrained image deblurring. This framework can be viewed as a generalization of proximal splitting method, which provides a common update strategy by exploiting second derivative information. This is achieved through utilizing the sparse pattern of inverse Hessian matrix. To alleviate the difficulties of the weighted least squares problem, an approximate solution is derived. Some theoretical aspects related to the proposed method are also discussed. Numerical experiments on various blurring conditions demonstrate the advantage of the proposed method in comparison to other iterative shrinkage-thresholding algorithms.

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Notes

  1. 1.

    http://www.lx.it.pt/~bioucas/code/TwIST_v2.zip.

  2. 2.

    http://ie.technion.ac.il/~becka/papers/tv_fista.zip.

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (Grant Nos.61175028, 61603249 and 61271317) and the Ph.D. Programs Foundation of Ministry of Education of China (Grant No. 20090073110045). The authors also thank the excellent works of Dr. Jose M. Bioucas Dias and Dr. Amir by making their source codes public available.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

    Zhongliang Jing & Han Pan

  2. University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yuankai Li

  3. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Peng Dong

Authors
  1. Zhongliang Jing
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  2. Han Pan
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  3. Yuankai Li
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  4. Peng Dong
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Jing, Z., Pan, H., Li, Y., Dong, P. (2018). Constrained Image Deblurring with Sparse Proximal Newton Splitting Method. In: Non-Cooperative Target Tracking, Fusion and Control. Information Fusion and Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-90716-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-90716-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90715-4

  • Online ISBN: 978-3-319-90716-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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