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Hybrid Function Sparse Representation Towards Image Super Resolution

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 11679))

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Abstract

Sparse representation with training-based dictionary has been shown successful on super resolution(SR) but still have some limitations. Based on the idea of making the magnification of function curve without losing its fidelity, we proposed a function based dictionary on sparse representation for super resolution, called hybrid function sparse representation (HFSR). The dictionary we designed is directly generated by preset hybrid functions without additional training, which can be scaled to any size as is required due to its scalable property. We mixed approximated Heaviside function (AHF), sine function and DCT function as the dictionary. Multi-scale refinement is then proposed to utilize the scalable property of the dictionary to improve the results. In addition, a reconstruct strategy is adopted to deal with the overlaps. The experiments on ‘Set14’ SR dataset show that our method has an excellent performance particularly with regards to images containing rich details and contexts compared with non-learning based state-of-the art methods.

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Author information

Authors and Affiliations

  1. ShanghaiTech University, School of Information Science and Technology, Shanghai, 201210, China

    Junyi Bian, Baojun Lin & Ke Zhang

  2. Chinese Academy of Sciences, Shanghai Engineering Center for Microsatellites, Shanghai, 201203, China

    Baojun Lin

Authors
  1. Junyi Bian

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  2. Baojun Lin

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  3. Ke Zhang

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Corresponding author

Correspondence toBaojun Lin.

Editor information

Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Gennaro Percannella

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Bian, J., Lin, B., Zhang, K. (2019). Hybrid Function Sparse Representation Towards Image Super Resolution. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_3

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