Hybrid edge and feature-based single-image superresolution

Mohammad Moinul Islam; Mohammed Nazrul Islam; Vijayan K. Asari; Mohammad Ataul Karim

doi:10.1117/1.JEI.25.4.043005

12 July 2016 Hybrid edge and feature-based single-image superresolution

Mohammad Moinul Islam, Mohammed Nazrul Islam, Vijayan K. Asari, Mohammad Ataul Karim

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 4, 043005 (July 2016). https://doi.org/10.1117/1.JEI.25.4.043005

Abstract

A neighborhood-dependent component feature learning method for regression analysis in single-image superresolution is presented. Given a low-resolution input, the method uses a directional Fourier phase feature component to adaptively learn the regression kernel based on local covariance to estimate the high-resolution image. The unique feature of the proposed method is that it uses image features to learn about the local covariance from geometric similarity between the low-resolution image and its high-resolution counterpart. For each patch in the neighborhood, we estimate four directional variances to adapt the interpolated pixels. This gives us edge information and Fourier phase gives features, which are combined to interpolate using kernel regression. In order to compare quantitatively with other state-of-the-art techniques, root-mean-square error and measure mean-square similarity are computed for the example images, and experimental results show that the proposed algorithm outperforms similar techniques available in the literature, especially at higher resolution scales.

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Mohammad Moinul Islam, Mohammed Nazrul Islam, Vijayan K. Asari, and Mohammad Ataul Karim "Hybrid edge and feature-based single-image superresolution," Journal of Electronic Imaging 25(4), 043005 (12 July 2016). https://doi.org/10.1117/1.JEI.25.4.043005

Published: 12 July 2016

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