Debluring low-resolution images

Jinshan Pan; Zhe Hu; Zhixun Su; Ming Hsuan Yang

doi:10.1007/978-3-319-54407-6_8

Debluring low-resolution images

Jinshan Pan, Zhe Hu, Zhixun Su, Ming Hsuan Yang

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

The recent years have witnessed significant advances in image deblurring. In general, the success of deblurring methods depends heavily on extraction of salient structures from a blurry image for kernel estimation. Most deblurring methods often operate on high-resolution images where contours or edges can be extracted for kernel estimation. However, recovering reliable structures from low-resolution images becomes extremely challenging. In this paper, we propose a spatially variant deblurring algorithm for low-resolution images based on the exemplars. To exploit the exemplar information, we develop a super-resolution guided method to help the restoration of reliable image structures which can be used for kernel estimation. Experimental evaluations against the state-of-the-art methods show that the proposed algorithm performs favorably in deblurring low-resolution images. Furthermore, we show that the SR results obtained as byproducts in our method are comparable compared to other blind SR methods.

Original language	English
Title of host publication	Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers
Editors	Jiwen Lu, Kai-Kuang Ma, Chu-Song Chen
Publisher	Springer Verlag
Pages	111-127
Number of pages	17
ISBN (Print)	9783319544069
DOIs	https://doi.org/10.1007/978-3-319-54407-6_8
Publication status	Published - 2017
Event	13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan, Province of China Duration: 2016 Nov 20 → 2016 Nov 24

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10116 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	13th Asian Conference on Computer Vision, ACCV 2016
Country	Taiwan, Province of China
City	Taipei
Period	16/11/20 → 16/11/24

Bibliographical note

Funding Information:
This work has been supported in part by NSF CAREER (No. 1149783), NSF IIS (No. 1152576), NSFC (No. 61572099 and 61320106008) and a gift from Adobe.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-54407-6_8

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Cite this

Pan, J., Hu, Z., Su, Z., & Yang, M. H. (2017). Debluring low-resolution images. In J. Lu, K-K. Ma, & C-S. Chen (Eds.), Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers (pp. 111-127). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10116 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-54407-6_8

Pan, Jinshan ; Hu, Zhe ; Su, Zhixun ; Yang, Ming Hsuan. / Debluring low-resolution images. Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers. editor / Jiwen Lu ; Kai-Kuang Ma ; Chu-Song Chen. Springer Verlag, 2017. pp. 111-127 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The recent years have witnessed significant advances in image deblurring. In general, the success of deblurring methods depends heavily on extraction of salient structures from a blurry image for kernel estimation. Most deblurring methods often operate on high-resolution images where contours or edges can be extracted for kernel estimation. However, recovering reliable structures from low-resolution images becomes extremely challenging. In this paper, we propose a spatially variant deblurring algorithm for low-resolution images based on the exemplars. To exploit the exemplar information, we develop a super-resolution guided method to help the restoration of reliable image structures which can be used for kernel estimation. Experimental evaluations against the state-of-the-art methods show that the proposed algorithm performs favorably in deblurring low-resolution images. Furthermore, we show that the SR results obtained as byproducts in our method are comparable compared to other blind SR methods.",

author = "Jinshan Pan and Zhe Hu and Zhixun Su and Yang, {Ming Hsuan}",

note = "Funding Information: This work has been supported in part by NSF CAREER (No. 1149783), NSF IIS (No. 1152576), NSFC (No. 61572099 and 61320106008) and a gift from Adobe.; 13th Asian Conference on Computer Vision, ACCV 2016 ; Conference date: 20-11-2016 Through 24-11-2016",

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Pan, J, Hu, Z, Su, Z & Yang, MH 2017, Debluring low-resolution images. in J Lu, K-K Ma & C-S Chen (eds), Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10116 LNCS, Springer Verlag, pp. 111-127, 13th Asian Conference on Computer Vision, ACCV 2016, Taipei, Taiwan, Province of China, 16/11/20. https://doi.org/10.1007/978-3-319-54407-6_8

Debluring low-resolution images. / Pan, Jinshan; Hu, Zhe; Su, Zhixun; Yang, Ming Hsuan.

Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers. ed. / Jiwen Lu; Kai-Kuang Ma; Chu-Song Chen. Springer Verlag, 2017. p. 111-127 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10116 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This work has been supported in part by NSF CAREER (No. 1149783), NSF IIS (No. 1152576), NSFC (No. 61572099 and 61320106008) and a gift from Adobe.

PY - 2017

Y1 - 2017

N2 - The recent years have witnessed significant advances in image deblurring. In general, the success of deblurring methods depends heavily on extraction of salient structures from a blurry image for kernel estimation. Most deblurring methods often operate on high-resolution images where contours or edges can be extracted for kernel estimation. However, recovering reliable structures from low-resolution images becomes extremely challenging. In this paper, we propose a spatially variant deblurring algorithm for low-resolution images based on the exemplars. To exploit the exemplar information, we develop a super-resolution guided method to help the restoration of reliable image structures which can be used for kernel estimation. Experimental evaluations against the state-of-the-art methods show that the proposed algorithm performs favorably in deblurring low-resolution images. Furthermore, we show that the SR results obtained as byproducts in our method are comparable compared to other blind SR methods.

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BT - Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers

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Pan J, Hu Z, Su Z, Yang MH. Debluring low-resolution images. In Lu J, Ma K-K, Chen C-S, editors, Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers. Springer Verlag. 2017. p. 111-127. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-54407-6_8