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Motion Deblurring Using Super-Sparsity

  • Jingxiong Zhao
  • Haohua Zhao
  • Keting Zhang
  • Liqing Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

Motion blur is caused by the camera shake during the exposure in which the blur kernel describes the trace of shaking. Based on this generating process of the kernel , we observed that the distribution of the kernel obeys super-sparsity, as the natural images. Recent works mostly exploit various kinds of priors in their models, but focus on the the speed or a close-form formulation for convenience of mathematical calculation ignoring the intrinsic feature of the kernels and images. In this paper we propose a new model with super-sparse prior for the deblurring problem from one single image. Since the close-form formulation of this model doesn’t exist, we use a look-up table trick to approximate the solution. Qualitative and quantitative evaluation demonstrate that our model with super-sparse prior can produce stable and high-quality results.

Keywords

Motion Deblur Blind Deconvolution Super-sparsity 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jingxiong Zhao
    • 1
  • Haohua Zhao
    • 1
  • Keting Zhang
    • 1
  • Liqing Zhang
    • 1
  1. 1.MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityChina

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