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A novel super-resolution image and video reconstruction approach based on Newton-Thiele’s rational kernel in sparse principal component analysis

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Abstract

In this paper, we propose a new image and video sequences reconstruction approach, where the Newton-Thiele’s vector valued rational interpolation is combined with the sparse principal component analysis. Through observation of the degraded model, the reconstruction scheme is performed by two steps. Firstly, the sparse principal component analysis and the linear minimum mean square-error estimation method are used to remove the noise from the degraded image. And then, the Newton-Thiele’s vector valued rational interpolation is used to magnify the denoising result, by which the details and texture regions of image can be well preserved. By using this novel reconstruction model by Newton-Thiele’s rational kernel in sparse principal component analysis, the final reconstructed results not only have good visual effect, but also have rich texture details. In order to show the effectiveness and robustness of the proposed method, we have done plenty of experiments on images and video sequences, and the experimental results show that the proposed method can produce better high-quality resolution results, as compared with the state-of-the-art methods.

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Acknowledgments

The authors would like to thank the referees for their valuable comments and constructive suggestions which greatly help improve the clarity of the paper. This work is supported by the National Natural Science Foundation of China under Grant Nos. 61472466, 61502141, and 61070227, the NSFC-Guangdong Joint Foundation (Key Project) under Grant No. U1135003, the Anhui Provincial Natural Science Foundation under Grant No. 1508085QF128, and the Fundamental Research Funds for the Central Universities under Grant No. JZ2015HGXJ0175.

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Authors and Affiliations

  1. School of Computer and Information, School of Mathematics, Hefei University of Technology, Hefei, 230009, China

    Lei He, Jieqing Tan & Xing Huo

  2. Institute of Intelligent Machines, Chinese Academy of Science, Hefei, China

    Chengjun Xie

Authors
  1. Lei He
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  2. Jieqing Tan
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  3. Xing Huo
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  4. Chengjun Xie
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Correspondence to Lei He.

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He, L., Tan, J., Huo, X. et al. A novel super-resolution image and video reconstruction approach based on Newton-Thiele’s rational kernel in sparse principal component analysis. Multimed Tools Appl 76, 9463–9483 (2017). https://doi.org/10.1007/s11042-016-3557-1

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  • DOI: https://doi.org/10.1007/s11042-016-3557-1

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