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Transfer Learning Based on A+ for Image Super-Resolution

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Knowledge Science, Engineering and Management (KSEM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9983))

Abstract

Example learning-based super-resolution (SR) methods are effective to generate a high-resolution (HR) image from a single low-resolution (LR) input. And these SR methods have shown a great potential for many practical applications. Unfortunately, most of popular example learning-based approaches extract features from limited training images. These training images are insufficient for super resolution task. Our work is to transfer some supplemental information from other domains. Therefore, in this paper, a new algorithm Transfer Learning based on A+ (TLA) is proposed for image super-resolution task. First, we transfer supplemental information from other datasets to construct a new dictionary. Then, in sample selection, more training samples are supplemented to the basic training samples. In experiments, we seek to explore what types of images can provide more appropriate information for super-resolution task. Experimental results indicate that our approach is superior to A+ when transferring images containing similar content with original data.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61502311), the Natural Science Foundation of Guangdong Province (No. 2016A030310053), the Science and Technology Innovation Commission of Shenzhen under Grant (No. JCYJ20150324141711640), the Strategic Emerging Industry Development Foundation of Shenzhen (No. JCY20130326105637578), the Shenzhen University research funding (201535), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), and the Tencent Rhinoceros Birds Scientific Research Foundation (2015).

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Authors and Affiliations

  1. Research Institute for Future Media Computing, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, People’s Republic of China

    Mei Su, Sheng-hua Zhong & Jian-min Jiang

Authors
  1. Mei Su
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  2. Sheng-hua Zhong
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  3. Jian-min Jiang
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Corresponding author

Correspondence to Sheng-hua Zhong .

Editor information

Editors and Affiliations

  1. University of Passau, Passau, Germany

    Franz Lehner

  2. University of Passau , Passau, Germany

    Nora Fteimi

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Su, M., Zhong, Sh., Jiang, Jm. (2016). Transfer Learning Based on A+ for Image Super-Resolution. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-47650-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47649-0

  • Online ISBN: 978-3-319-47650-6

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