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First-order derivative-based super-resolution

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Abstract

A single fast super-resolution method based on first-order derivatives from neighbor pixels is proposed. The basic idea of the proposed method is to exploit a first-order derivatives component of six edge directions around a missing pixel, followed by back projection to reduce noise estimated by the difference between simulated and observed images. Using first-order derivatives as a feature, the proposed method is expected to have low computational complexity, and it can theoretically reduce blur, blocking, and ringing artifacts in edge areas compared to previous methods. Experiments were conducted using 900 natural grayscale images from the USC-SIPI Database. We evaluated the proposed and previous methods using peak signal-to-noise ratio, structural similarity, feature similarity, and computation time. Experimental results indicate that the proposed method clearly outperforms other state-of-the-art algorithms such as fast curvature-based interpolation.

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Acknowledgments

This work was supported by CREST, Japan Science and Technology Agency. We would like to thank the Indonesia Endowment Fund for Education (LPDP) Scholarships from the Ministry of Finance, The Republic of Indonesia for doctoral degree scholarship.

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Authors and Affiliations

  1. Intelligent Interaction Technologies Department, University of Tsukuba, Tennoudai 1-1-1, Tsukuba, 305-8573, Japan

    Muhammad Haris & Hajime Nobuhara

  2. Computer Science Faculty, University of Indonesia, Depok, Indonesia

    M. Rahmat Widyanto

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  1. Muhammad Haris
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  2. M. Rahmat Widyanto
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  3. Hajime Nobuhara
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Correspondence to Muhammad Haris.

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Haris, M., Widyanto, M.R. & Nobuhara, H. First-order derivative-based super-resolution. SIViP 11, 1–8 (2017). https://doi.org/10.1007/s11760-016-0880-y

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  • DOI: https://doi.org/10.1007/s11760-016-0880-y

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