Robust Kernel Estimation with Outliers Handling for Image Deblurring

Jinshan Pan, Zhouchen Lin, Zhixun Su, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

72 Citations (Scopus)


Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers, such as saturated pixels and non-Gaussian noise, are present. While some existing non-blind deblurring algorithms can deal with outliers to a certain extent, few blind deblurring methods are developed to well estimate the blur kernels from the blurred images with outliers. In this paper, we present an algorithm to address this problem by exploiting reliable edges and removing outliers in the intermediate latent images, thereby estimating blur kernels robustly. We analyze the effects of outliers on kernel estimation and show that most state-of-the-art blind deblurring methods may recover delta kernels when blurred images contain significant outliers. We propose a robust energy function which describes the properties of outliers for the final latent image restoration. Furthermore, we show that the proposed algorithm can be applied to improve existing methods to deblur images with outliers. Extensive experiments on different kinds of challenging blurry images with significant amount of outliers demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PublisherIEEE Computer Society
Number of pages9
ISBN (Electronic)9781467388504
Publication statusPublished - 2016 Dec 9
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: 2016 Jun 262016 Jul 1

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Country/TerritoryUnited States
CityLas Vegas

Bibliographical note

Funding Information:
J. Pan is supported by a scholarship from China Scholarship Council. Z. Lin is supported by National Basic Research Program of China (973 Program) (No. 2015CB352502), NSFC (No. 61272341 and 61231002), and MSRA. Z. Su is supported by the NSFC (No. 61572099 and 61320106008). M.- H. Yang is supported in part by the NSF CAREER Grant (No. 1149783), NSF IIS Grant (No. 1152576), and a gift from Adobe.

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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