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Patch Selection for Single Image Deblurring Based on a Coalitional Game

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9475))

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Abstract

Most single-image deblurring methods estimate the blur kernel using whole image, however, that may lead to incorrect estimation and more computations. In this paper, we focus on accelerating the blind deconvolution algorithm and increasing the accuracy of kernel estimation by using only a small region in image to perform the process of kernel estimation. Then, the problem now is to find the most proper region. At first, we found informative pixels to locate useful patches. Inspiring by game theory, we propose a coalitional game based patch selection method to choose a group of patches for kernel estimation. In this game, each patch represents a player, and our purpose is to find a coalition that has the maximal payoff. Shapley Value is applied to fairly distribute the utility to each player. We show the speed-up and the quality improvement of our method both on real-world and synthetic images.

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Acknowledgment

This study is conducted under the “Online and Offline integrated Smart Commerce Platform (2/4)” of the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs of the Republic of China

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Authors and Affiliations

  1. The Innovative DigiTech-Enabled Applications and Services Institute (IDEAS), Institute for Information Industry (III), Taipei, Taiwan

    Jung-Hsuan Lin, Rong-Sheng Wang & Jing-wei Wang

Authors
  1. Jung-Hsuan Lin
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  2. Rong-Sheng Wang
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  3. Jing-wei Wang
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Corresponding author

Correspondence to Jung-Hsuan Lin .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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© 2015 Springer International Publishing Switzerland

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Lin, JH., Wang, RS., Wang, Jw. (2015). Patch Selection for Single Image Deblurring Based on a Coalitional Game. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_48

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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