Training-based Image Upsampling using Kernels

  • Authors

    • Seungjong Kim
    https://doi.org/10.14419/ijet.v8i1.4.25135

    Received date: December 31, 2018

    Accepted date: December 31, 2018

    Published date: January 2, 2019

  • Image upscaling, least squares method, kernel design, image training.

  • Abstract

    This paper proposes an issue of upsampling which populates missing pixel components at the zero-padded images. This process is called image upsampling (or image reconstruction). In this paper, a kernel design approach is studied in detail, which is obtained based on least-squares method by exploiting training set to achieve desired upsampling performance. To meet the complexity requirement, tradeoff approach is considered during the kernel design. Performance of the proposed method is compared with that of other conventional upsampling methods such as nearest neighbor, bilinear interpolation, and bicubic interpolation, in terms of objective and subjective quality metrics. Simulation results inform that the proposed method provides outstanding performance when it is compared with conventional methods.

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  • How to Cite

    Kim, S. (2019). Training-based Image Upsampling using Kernels. International Journal of Engineering and Technology, 8(1.4), 73-81. https://doi.org/10.14419/ijet.v8i1.4.25135