Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration

  • Liu, Ganghua (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Tian, Wei (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Luo, Yushun (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Zou, Juncheng (Department of Support Services, The State Grid Chongqing Electric Power Company) ;
  • Tang, Shu (College of Computer Science and Technology, Chongqing University of Posts and Telecommunications)
  • Received : 2021.07.13
  • Accepted : 2021.10.10
  • Published : 2022.02.28
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Abstract

Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.

Keywords

Acknowledgement

All the authors will thank D. Krishnan, J. S. Pan, and L. Chen for offering their codes respectively.

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