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Blind deblurring using discriminative image smoothing
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2018 (English)In: 1st Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, Springer Verlag , 2018, p. 490-500Conference paper, Published paper (Refereed)
Abstract [en]

This paper aims to exploit the full potential of gradient-based methods, attempting to explore a simple, robust yet discriminative image prior for blind deblurring. The specific contributions are three-fold: Above all, a pure gradient-based heavy-tailed model is proposed as a generalized integration of the normalized sparsity and the relative total variation. On the second, a plug-and-play algorithm is deduced to alternatively estimate the intermediate sharp image and the nonparametric blur kernel. With the numerical scheme, image estimation is simplified to an image smoothing problem. Lastly, a great many experiments are performed accompanied with comparisons with state-of-the-art approaches on synthetic benchmark datasets and real blurry images in various scenarios. The experimental results show well the effectiveness and robustness of the proposed method. 

Place, publisher, year, edition, pages
Springer Verlag , 2018. p. 490-500
Keywords [en]
Blind deblurring, Discriminative prior, Low-illumination, Computer vision, Gradient-based method, Image estimation, Low illuminations, Numerical scheme, State-of-the-art approach, Synthetic benchmark, Image enhancement
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247475DOI: 10.1007/978-3-030-03398-9_42Scopus ID: 2-s2.0-85057075196ISBN: 9783030033972 (print)OAI: oai:DiVA.org:kth-247475DiVA, id: diva2:1313300
Conference
23 November 2018 through 26 November 2018
Note

QC20190502

Available from: 2019-05-03 Created: 2019-05-03 Last updated: 2019-05-03Bibliographically approved

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Li, Haibo

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Total: 94 hits
CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf