Journal of Mathematical Imaging and Vision

, Volume 52, Issue 2, pp 234–248 | Cite as

Motion Deblurring Using Non-stationary Image Modeling

  • Wen-Ze ShaoEmail author
  • Qi Ge
  • Hai-Song Deng
  • Zhi-Hui Wei
  • Hai-Bo Li


It is well-known that shaken cameras or mobile phones during exposure usually lead to motion blurry photographs. Therefore, camera shake deblurring or motion deblurring is required and requested in many practical scenarios. The contribution of this paper is the proposal of a simple yet effective approach for motion blur kernel estimation, i.e., blind motion deblurring. Though there have been proposed several methods for motion blur kernel estimation in the literature, we impose a type of non-stationary Gaussian prior on the gradient fields of sharp images, in order to automatically detect and purse the salient edges of images as the important clues to blur kernel estimation. On one hand, the prior is able to promote sparsity inherited in the non-stationarity of the precision parameters (inverse of variances). On the other hand, since the prior is in a Gaussian form, there exists a great possibility of deducing a conceptually simple and computationally tractable inference scheme. Specifically, the well-known expectation–maximization algorithm is used to alternatingly estimate the motion blur kernels, the salient edges of images as well as the precision parameters in the image prior. In difference from many existing methods, no hyperpriors are imposed on any parameters in this paper; there are not any pre-processing steps involved in the proposed method, either, such as explicit suppression of random noise or prediction of salient edge structures. With estimated motion blur kernels, the deblurred images are finally generated using an off-the-shelf non-blind deconvolution method proposed by Krishnan and Fergus (Adv Neural Inf Process Syst 22:1033–1041, 2009). The rationality and effectiveness of our proposed method have been well demonstrated by the experimental results on both synthetic and realistic motion blurry images, showing state-of-the-art blind motion deblurring performance of the proposed approach in the term of quantitative metric as well as visual perception.


Blind motion deblurring Camera shake  Blur kernel estimation Expectation–maximization  Split Bregman iteration 



Many thanks are given to the anonymous reviewers for their serious, pertinent and helpful comments significantly strengthening this paper. Wen-Ze Shao is grateful to Professor Michael Elad for the financial support allowing him to work at Department of Computer Science, Technion-Israel Institute of Technology, as well as Professor Yi-Zhong Ma and Dr. Min Wu for their kind supports in the past years. He would also like to show many thanks to Mr. Ya-Tao Zhang and other kind people for helping him through his lost and sad years. The work is supported in part by the National Natural Science Foundation (NSF) of China for Youth under Grant No. 61402239, the NSF of Jiangsu Province for Youth under Grant No. BK20130868, the NSF of Jiangsu Higher Education Institutions under Grant No. 13KJB510022 and 13KJB120005, and the NSF of Nanjing University of Posts and Telecommunications under Grant No. NY212014, NY213007.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Wen-Ze Shao
    • 1
    • 2
    Email author
  • Qi Ge
    • 1
  • Hai-Song Deng
    • 3
  • Zhi-Hui Wei
    • 4
  • Hai-Bo Li
    • 5
  1. 1.College of Telecommunications and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Department of Computer ScienceTechnion-Israel Institute of TechnologyHaifaIsrael
  3. 3.School of Mathematics and StatisticsNanjing Audit UniversityNanjingChina
  4. 4.School of Computer Science and TechnologyNanjing University of Science and TechnologyNanjingChina
  5. 5.School of Computer Science and CommunicationKTH Royal Institute of TechnologyStockholmSweden

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