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Superresolution reconstruction using nonlinear gradient-based regularization

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Abstract

This paper discusses the problem of superresolution reconstruction. To preserve edges accurately and efficiently in the reconstruction, we propose a nonlinear gradient-based regularization that uses the gradient vector field of a preliminary high resolution image to configure a regularization matrix and compute the regularization parameters. Compared with other existing methods, it not only enhances the spatial resolution of the resulting images, but can also preserve edges and smooth noise to a greater extent. The advantages are shown in simulations and experiments with synthetic and real images.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Kowloon, Hong Kong

    Xin Zhang & Edmund Y. Lam

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  1. Xin Zhang
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  2. Edmund Y. Lam
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Correspondence to Xin Zhang.

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Zhang, X., Lam, E.Y. Superresolution reconstruction using nonlinear gradient-based regularization. Multidim Syst Sign Process 20, 375–384 (2009). https://doi.org/10.1007/s11045-008-0072-1

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  • DOI: https://doi.org/10.1007/s11045-008-0072-1

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