Debluring low-resolution images

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationComputer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers
EditorsJiwen Lu, Kai-Kuang Ma, Chu-Song Chen
PublisherSpringer Verlag
Pages111-127
Number of pages17
ISBN (Print)9783319544069
DOIs
Publication statusPublished - 2017 Jan 1
Event13th Asian Conference on Computer Vision, ACCV 2016 - Taipei, Taiwan, Province of China
Duration: 2016 Nov 202016 Nov 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10116 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Asian Conference on Computer Vision, ACCV 2016
CountryTaiwan, Province of China
City Taipei
Period16/11/2016/11/24

Fingerprint

Image resolution
Deblurring
Kernel Estimation
Restoration
Image Deblurring
Byproducts
Super-resolution
Experimental Evaluation
High Resolution

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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)).
@inproceedings{28b59109569b48369b89618aa764a562,
title = "Debluring low-resolution images",
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}",
year = "2017",
month = "1",
day = "1",
doi = "10.1007/978-3-319-54407-6_8",
language = "English",
isbn = "9783319544069",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "111--127",
editor = "Jiwen Lu and Kai-Kuang Ma and Chu-Song Chen",
booktitle = "Computer Vision - ACCV 2016 Workshops - ACCV 2016 International Workshops, Revised Selected Papers",
address = "Germany",

}

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 proceedingConference contribution

TY - GEN

T1 - Debluring low-resolution images

AU - Pan, Jinshan

AU - Hu, Zhe

AU - Su, Zhixun

AU - Yang, Ming Hsuan

PY - 2017/1/1

Y1 - 2017/1/1

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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85016170335&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016170335&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-54407-6_8

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

M3 - Conference contribution

AN - SCOPUS:85016170335

SN - 9783319544069

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 111

EP - 127

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

A2 - Lu, Jiwen

A2 - Ma, Kai-Kuang

A2 - Chen, Chu-Song

PB - Springer Verlag

ER -

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