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Single image super-resolution by combining self-learning and example-based learning methods

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Abstract

In this paper we propose a novel method for single image super-resolution (SISR) by combining self-learning method and example-based learning method. The self-learning method we used which is proposed by Zeyde et al. (2012) has the ability to scale-up a single image from the given low-resolution (LR) image itself by learning a dictionary pair directly from the given LR image (as high-resolution image) and its scaled-down version (as LR image). This is the so-called boot-strapping method in Zeyde, Elad, Protter (Lect Notes Comput Sci 6920:711–730, 2012). With the output image obtained by the boot-strapping method and the original high-resolution (HR) image, we can get a super-resolution image by learning the sparse representation model proposed in Na, Jinye, Xuan, Xiaoyi (Multimed Tools Appl 74:1997–2007, 2015). Our combined approach shows to perform better even when there are little training example images. A number of experimental results on true images show that our method gains both visual and PSNR improvements.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnic University, Xian, 710129, People’s Republic China

    Na Ai, Jinye Peng & Xiaoyi Feng

  2. School of Information and Technology, Northwest University, Xian, 710127, People’s Republic China

    Na Ai, Jinye Peng & Xuan Zhu

Authors
  1. Na Ai
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  2. Jinye Peng
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  3. Xuan Zhu
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  4. Xiaoyi Feng
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Corresponding author

Correspondence to Na Ai.

Additional information

This work was supported by Science Foundation of Northwest University (ND10010).

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Ai, N., Peng, J., Zhu, X. et al. Single image super-resolution by combining self-learning and example-based learning methods. Multimed Tools Appl 75, 6647–6662 (2016). https://doi.org/10.1007/s11042-015-2597-2

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  • DOI: https://doi.org/10.1007/s11042-015-2597-2

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