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Learning adaptive interpolation kernels for fast single-image super resolution

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Abstract

This paper presents a fast single-image super-resolution approach that involves learning multiple adaptive interpolation kernels. Based on the assumptions that each high-resolution image patch can be sparsely represented by several simple image structures and that each structure can be assigned a suitable interpolation kernel, our approach consists of the following steps. First, we cluster the training image patches into several classes and train each class-specific interpolation kernel. Then, for each input low-resolution image patch, we select few suitable kernels of it to make up the final interpolation kernel. Since the proposed approach is mainly based on simple linear algebra computations, its efficiency can be guaranteed. And experimental comparisons with state-of-the-art super-resolution reconstruction algorithms on simulated and real-life examples can validate the performance of our proposed approach.

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Notes

  1. The source codes of our proposed SRR approach can be downloaded at http://mda.ia.ac.cn/people/huxy/oproj/fastsrr.htm.

  2. The codes for the SP-SR method used for comparison can be downloaded from the authors’ homepage at http://www.ifp.illinois.edu/~jyang29/resources.html. The RMSE and SSIM values of pictures flower and girl are copied from [12] directly.

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Acknowledgments

The authors wish to thank the anonymous reviewers for their insightful comments, which helped us improve the quality of the paper significantly

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing , 100190, People’s Republic of China

    Xiyuan Hu & Silong Peng

  2. Institute of Information Science, Academia Sinica, Taipei , 11529, Taiwan, ROC

    Wen-Liang Hwang

  3. Department of Information Management, Kainan University, Taoyuan County , 33857, Taiwan, ROC

    Wen-Liang Hwang

Authors
  1. Xiyuan Hu
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  2. Silong Peng
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  3. Wen-Liang Hwang
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Corresponding author

Correspondence to Silong Peng.

Additional information

This work was supported by the National Natural Science Foundation of China (61032007, 61101219, 61201375) and the National High Technology R&D Program of China (863 Program) (Grant No. 2013AA014602)

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Hu, X., Peng, S. & Hwang, WL. Learning adaptive interpolation kernels for fast single-image super resolution. SIViP 8, 1077–1086 (2014). https://doi.org/10.1007/s11760-014-0634-7

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