Blind Image Deblurring Using Elastic-Net Based Rank Prior

  • Hongyan Wang
  • Jinshan Pan
  • Zhixun SuEmail author
  • Songxin Liang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)


In this paper, we propose a new image prior for blind image deblurring. The proposed prior exploits similar patches of an image and it is based on an elastic-net regularization of singular values. We quantitatively verify that it favors clear images over blurred images. This property is able to facilitate the kernel estimation in the conventional maximum a posterior framework. Based on this prior, we develop an efficient optimization method to solve the proposed model. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.


Kernel Estimation Clear Image Image Denoising Nuclear Norm Recovered Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by National Natural Science Foundation of China (No. 61572099, 51379033, and 51522902) and National Science and Technology Major Project (No. ZX20140419 and 2014ZX04001011).

Supplementary material (26.4 mb)
Supplementary material 1 (zip 27003 KB)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hongyan Wang
    • 1
  • Jinshan Pan
    • 1
  • Zhixun Su
    • 1
    Email author
  • Songxin Liang
    • 1
  1. 1.School of Mathematical SciencesDalian University of TechnologyDalianChina

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