Handling motion blur in multi-frame super-resolution

Ziyang Ma; Renjie Liao; Xin Tao; Li Xu; Jiaya Jia; Enhua Wu

doi:10.1109/CVPR.2015.7299159

Handling motion blur in multi-frame super-resolution

Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

136 Citations (Scopus)

Abstract

Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.

Original language	English
Title of host publication	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Publisher	IEEE Computer Society
Pages	5224-5232
Number of pages	9
ISBN (Electronic)	9781467369640
DOIs	https://doi.org/10.1109/CVPR.2015.7299159
Publication status	Published - 14 Oct 2015
Externally published	Yes
Event	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States Duration: 7 Jun 2015 → 12 Jun 2015

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	07-12-June-2015
ISSN (Print)	1063-6919

Conference

Conference	IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Country/Territory	United States
City	Boston
Period	7/06/15 → 12/06/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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10.1109/CVPR.2015.7299159

Cite this

Ma, Z., Liao, R., Tao, X., Xu, L., Jia, J., & Wu, E. (2015). Handling motion blur in multi-frame super-resolution. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 (pp. 5224-5232). Article 7299159 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 07-12-June-2015). IEEE Computer Society. https://doi.org/10.1109/CVPR.2015.7299159

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title = "Handling motion blur in multi-frame super-resolution",

abstract = "Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.",

author = "Ziyang Ma and Renjie Liao and Xin Tao and Li Xu and Jiaya Jia and Enhua Wu",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 ; Conference date: 07-06-2015 Through 12-06-2015",

year = "2015",

month = oct,

day = "14",

doi = "10.1109/CVPR.2015.7299159",

language = "English",

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Ma, Z, Liao, R, Tao, X, Xu, L, Jia, J & Wu, E 2015, Handling motion blur in multi-frame super-resolution. in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015., 7299159, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June-2015, IEEE Computer Society, pp. 5224-5232, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, United States, 7/06/15. https://doi.org/10.1109/CVPR.2015.7299159

Handling motion blur in multi-frame super-resolution. / Ma, Ziyang; Liao, Renjie; Tao, Xin et al.
IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society, 2015. p. 5224-5232 7299159 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 07-12-June-2015).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

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PY - 2015/10/14

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N2 - Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.

AB - Ubiquitous motion blur easily fails multi-frame super-resolution (MFSR). Our method proposed in this paper tackles this issue by optimally searching least blurred pixels in MFSR. An EM framework is proposed to guide residual blur estimation and high-resolution image reconstruction. To suppress noise, we employ a family of sparse penalties as natural image priors, along with an effective solver. Theoretical analysis is performed on how and when our method works. The relationship between estimation errors of motion blur and the quality of input images is discussed. Our method produces sharp and higher-resolution results given input of challenging low-resolution noisy and blurred sequences.

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M3 - Conference Paper published in a book

T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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BT - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015

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ER -

Handling motion blur in multi-frame super-resolution

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