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Non-Blind Image Deblurring Method by the Total Variation Deep Network
Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China..
Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China..
Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing 210003, Jiangsu, Peoples R China..
Univ Leicester, Dept Informat, Leicester LE1 7RH, Leics, England..
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 37536-37544Article in journal (Refereed) Published
Abstract [en]

There are a lot of non-blind image deblurring methods, especially with the total variation (TV) model-based method. However, how to choose the parameters adaptively for regularization is a major open problem. We proposed a very novel method that is based on the TV deep network to learn the best parameters adaptively for regularization. We used deep learning and prior knowledge to set up a TV-based deep network and calculate the parameters of regularization, such as biases and weights. Therefore, we used the idea of a deep network to update these parameters automatically to avoid sophisticated calculations. Our experimental results by our proposed network are significantly better than several other methods, in respect of detail retention and anti-noise performance. At the same time, we can achieve the same effect with a minimum number of training sets, thus speeding up the calculation.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 7, p. 37536-37544
Keywords [en]
Non-blind image deblurring, total variation model, deep learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-249827DOI: 10.1109/ACCESS.2019.2891626ISI: 000463637800001OAI: oai:DiVA.org:kth-249827DiVA, id: diva2:1306094
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QC 20190423

Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-04-23Bibliographically approved

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Li, Haibo

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  • de-DE
  • en-GB
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  • fi-FI
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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