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Adaptive directional decomposition in non sub sample contourlet transform domain for single image super resolution

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Abstract

The crucial point of research about single-image super-resolution is to enhance the size of an image without fiddle its quality. To conserve the quality of an enhanced image, a novel learning based approach in Non Sub Sampled Contourlet Transform (NSCT) domain is proposed in this paper. The NSCT constitute multiscale -multidirectional decomposition of an image. An algorithm, proposed for adaptive multidirectional decomposition picks up the optimal benefit of the directionality offered by NSCT. A sharp initialization, obtained via learning in NSCT domain, is able to generate a high resolution image with minimum edge artifacts. Experiments on various images show the effectiveness of the proposed algorithm.

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Acknowledgments

One of the author, Ms. Shah would like to thank Dr. Mita C. Paunwalla for her kind co-operation and encouragment.

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Authors and Affiliations

  1. C.K.P.C.E.T., Surat, India

    Amisha J. Shah

  2. FCIPT, Institute for Plasma Research, Gandhinagar, India

    Suryakant B. Gupta

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  1. Amisha J. Shah
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  2. Suryakant B. Gupta
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Correspondence to Amisha J. Shah.

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Shah, A.J., Gupta, S.B. Adaptive directional decomposition in non sub sample contourlet transform domain for single image super resolution. Multimed Tools Appl 75, 8443–8467 (2016). https://doi.org/10.1007/s11042-015-2765-4

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  • DOI: https://doi.org/10.1007/s11042-015-2765-4

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