Blind Image Deblurring with Outlier Handling

Jiangxin Dong, Jinshan Pan, Zhixun Su, Ming Hsuan Yang

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

12 Citations (Scopus)

Abstract

Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we propose a simple yet effective blind image deblurring algorithm to handle blurred images with outliers. The proposed method is motivated by the observation that outliers in the blurred images significantly affect the goodness-of-fit in function approximation. Therefore, we propose an algorithm to model the data fidelity term so that the outliers have little effect on kernel estimation. The proposed algorithm does not require any heuristic outlier detection step, which is critical to the state-of-the-art blind deblurring methods for images with outliers. We analyze the relationship between the proposed algorithm and other blind deblurring methods with outlier handling and show how to estimate intermediate latent images for blur kernel estimation principally. We show that the proposed method can be applied to generic image deblurring as well as non-uniform deblurring. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art blind image deblurring methods on both synthetic and real-world images.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2497-2505
Number of pages9
ISBN (Electronic)9781538610329
DOIs
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
Volume2017-October
ISSN (Print)1550-5499

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period17/10/2217/10/29

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Dong, J., Pan, J., Su, Z., & Yang, M. H. (2017). Blind Image Deblurring with Outlier Handling. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (pp. 2497-2505). [8237533] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2017.271
Dong, Jiangxin ; Pan, Jinshan ; Su, Zhixun ; Yang, Ming Hsuan. / Blind Image Deblurring with Outlier Handling. Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2497-2505 (Proceedings of the IEEE International Conference on Computer Vision).
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Dong, J, Pan, J, Su, Z & Yang, MH 2017, Blind Image Deblurring with Outlier Handling. in Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017., 8237533, Proceedings of the IEEE International Conference on Computer Vision, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 2497-2505, 16th IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 17/10/22. https://doi.org/10.1109/ICCV.2017.271

Blind Image Deblurring with Outlier Handling. / Dong, Jiangxin; Pan, Jinshan; Su, Zhixun; Yang, Ming Hsuan.

Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2497-2505 8237533 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October).

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

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AB - Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we propose a simple yet effective blind image deblurring algorithm to handle blurred images with outliers. The proposed method is motivated by the observation that outliers in the blurred images significantly affect the goodness-of-fit in function approximation. Therefore, we propose an algorithm to model the data fidelity term so that the outliers have little effect on kernel estimation. The proposed algorithm does not require any heuristic outlier detection step, which is critical to the state-of-the-art blind deblurring methods for images with outliers. We analyze the relationship between the proposed algorithm and other blind deblurring methods with outlier handling and show how to estimate intermediate latent images for blur kernel estimation principally. We show that the proposed method can be applied to generic image deblurring as well as non-uniform deblurring. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art blind image deblurring methods on both synthetic and real-world images.

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Dong J, Pan J, Su Z, Yang MH. Blind Image Deblurring with Outlier Handling. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2497-2505. 8237533. (Proceedings of the IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2017.271