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Single Image Super-Resolution via Convolutional Neural Network and Total Variation Regularization

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9517))

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Abstract

We propose a super-resolution reconstruction model based on the fusion of convolutional neural networks and regularization constraints. Our model not only takes advantage of the convolutional neural network’s prominent capability for nonlinear mapping between low-resolution and high-resolution images, but also takes the image inherent tendency to have bountiful repeated structural information into accounts. We derive our total variation regularization constraints based on the image local similarity and non-local similarity. Through coalescence of convolutional nerual network and delicately devised adaptive regularization constraints, our model yields a state-of-the-art restoration quality from a single image. Besides, our system can be expanded to tackle more low-level vision problems as well.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Xiamen University, Xiamen, China

    Yanyun Qu, Cuiting Shi, Junran Liu & Liying Peng

  2. School of Computer and Information Engineering, Xiamen University Techonology, Xiamen, China

    Xiaofeng Du

Authors
  1. Yanyun Qu
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  2. Cuiting Shi
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  3. Junran Liu
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  4. Liying Peng
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  5. Xiaofeng Du
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Corresponding author

Correspondence to Yanyun Qu .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Qu, Y., Shi, C., Liu, J., Peng, L., Du, X. (2016). Single Image Super-Resolution via Convolutional Neural Network and Total Variation Regularization. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-27674-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27673-1

  • Online ISBN: 978-3-319-27674-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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