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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2011  >  40(1): 105-110

WU Wei, YANG Xiao-min, YU Yan-mei, SHI Yi-xing, HE Xiao-hai. Image Super-Resolution Using KPLS[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 105-110. doi: 10.3969/j.issn.1001-0548.2011.01.020
Citation: WU Wei, YANG Xiao-min, YU Yan-mei, SHI Yi-xing, HE Xiao-hai. Image Super-Resolution Using KPLS[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 105-110. doi: 10.3969/j.issn.1001-0548.2011.01.020
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Image Super-Resolution Using KPLS

doi: 10.3969/j.issn.1001-0548.2011.01.020
  • 1.

    College of Electronics and Information Engineering,Sichuan University Chengdu 610064

  • Received Date: 2009-08-08
  • Rev Recd Date: 2010-05-19
  • Publish Date: 2011-02-15
  • A learning-based super-resolution algorithm based on Kernel Partial Least Squares (KPLS) regression is proposed. First, KPLS regression algorithm is introduced. Then a super-resolution algorithm based on KPLS regression is analyzed. High resolution images use the high-frequency information as their feature, while low resolution images use middle-frequency as their features. Based on the relationship of the high and low resolution images, KPLS is used to set up regression model. The regression model is applied to infer high-resolution image. The experimental results show that our method can achieve very good results to face images and car plate images. The results of our method are closer to the real images.
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  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Image Super-Resolution Using KPLS

doi: 10.3969/j.issn.1001-0548.2011.01.020
  • 1. College of Electronics and Information Engineering,Sichuan University Chengdu 610064
  • Received Date: 2009-08-08
  • Rev Recd Date: 2010-05-19
  • Publish Date: 2011-02-15

Abstract: A learning-based super-resolution algorithm based on Kernel Partial Least Squares (KPLS) regression is proposed. First, KPLS regression algorithm is introduced. Then a super-resolution algorithm based on KPLS regression is analyzed. High resolution images use the high-frequency information as their feature, while low resolution images use middle-frequency as their features. Based on the relationship of the high and low resolution images, KPLS is used to set up regression model. The regression model is applied to infer high-resolution image. The experimental results show that our method can achieve very good results to face images and car plate images. The results of our method are closer to the real images.

WU Wei, YANG Xiao-min, YU Yan-mei, SHI Yi-xing, HE Xiao-hai. Image Super-Resolution Using KPLS[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 105-110. doi: 10.3969/j.issn.1001-0548.2011.01.020
Citation: WU Wei, YANG Xiao-min, YU Yan-mei, SHI Yi-xing, HE Xiao-hai. Image Super-Resolution Using KPLS[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 105-110. doi: 10.3969/j.issn.1001-0548.2011.01.020
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