Learning Discriminative Data Fitting Functions for Blind Image Deblurring

Jinshan Pan, Jiangxin Dong, Yu Wing Tai, Zhixun Su, Ming Hsuan Yang

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

23 Citations (Scopus)


Solving blind image deblurring usually requires defining a data fitting function and image priors. While existing algorithms mainly focus on developing image priors for blur kernel estimation and non-blind deconvolution, only a few methods consider the effect of data fitting functions. In contrast to the state-of-the-art methods that use a single or a fixed data fitting term, we propose a data-driven approach to learn effective data fitting functions from a large set of motion blurred images with the associated ground truth blur kernels. The learned data fitting function facilitates estimating accurate blur kernels for generic scenes and domain-specific problems with corresponding image priors. In addition, we extend the learning approach for data fitting function to latent image restoration and nonuniform deblurring. Extensive experiments on challenging motion blurred images demonstrate the proposed algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Electronic)9781538610329
Publication statusPublished - 2017 Dec 22
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: 2017 Oct 222017 Oct 29

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Other16th IEEE International Conference on Computer Vision, ICCV 2017

Bibliographical note

Funding Information:
This work has been partially supported by NSFC (No. 61572099, 61522203), NSF CAREER (No. 1149783), 973 Program (No. 2014CB347600), NSF of Jiangsu Province (No. BK20140058), the National Key R&D Program of China (No. 2016YFB1001001), and gifts from Adobe and Nvidia.

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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