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Grey wolf optimization algorithm for facial image super-resolution

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Abstract

Face image super-resolution (FSR) algorithms are capable of providing a high-resolution image from an input low-resolution (LR) image. Various FSR algorithms use a set of training examples to reconstruct the input LR image. For the purpose, proper weights need to be calculated for each training image. In general, the least square estimation approach is used for obtaining optimal reconstruction weights, known as least square representation (LSR) problem. In this paper, to minimize LSR problem more effectively, a grey wolf optimizer (GWO) based FSR algorithm (FSR-GWO) is proposed. To make search process of GWO algorithm suitable to FSR, a new formulation for upper-bound and lower-bound is introduced. Performance comparison with state-of-the-art nature-inspired algorithms and several super-resolution methods on FEI public face database shows the effectiveness of the proposed FSR-GWO algorithm.

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    Authors and Affiliations

    1. Multimedia and Information Security Lab, ABV-Indian Institute of Information Technology and Management, Gwalior, 474015, India

      Shyam Singh Rajput, Vijay Kumar Bohat & K. V. Arya

    2. Department of Computer Science & Engineering, Institute of Engineering and Technology, Lucknow, 226021, India

      K. V. Arya

    Authors
    1. Shyam Singh Rajput
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    2. Vijay Kumar Bohat
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    3. K. V. Arya
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    Corresponding author

    Correspondence to Vijay Kumar Bohat.

    Additional information

    Shyam Singh Rajput and Vijay Kumar Bohat have done equal amount of work.

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    Appendix

    List of abbreviations are provided in Table 5.

    Table 5 List of abbreviations
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    Rajput, S.S., Bohat, V.K. & Arya, K.V. Grey wolf optimization algorithm for facial image super-resolution. Appl Intell 49, 1324–1338 (2019). https://doi.org/10.1007/s10489-018-1340-x

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