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Single image super-resolution based on approximated Heaviside functions and iterative refinement

Author

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  • Xin-Yu Wang
  • Ting-Zhu Huang
  • Liang-Jian Deng

Abstract

One method of solving the single-image super-resolution problem is to use Heaviside functions. This has been done previously by making a binary classification of image components as “smooth” and “non-smooth”, describing these with approximated Heaviside functions (AHFs), and iteration including l1 regularization. We now introduce a new method in which the binary classification of image components is extended to different degrees of smoothness and non-smoothness, these components being represented by various classes of AHFs. Taking into account the sparsity of the non-smooth components, their coefficients are l1 regularized. In addition, to pick up more image details, the new method uses an iterative refinement for the residuals between the original low-resolution input and the downsampled resulting image. Experimental results showed that the new method is superior to the original AHF method and to four other published methods.

Suggested Citation

  • Xin-Yu Wang & Ting-Zhu Huang & Liang-Jian Deng, 2018. "Single image super-resolution based on approximated Heaviside functions and iterative refinement," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-24, January.
    • Handle: RePEc:plo:pone00:0182240
    DOI: 10.1371/journal.pone.0182240
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