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

Xin-Yu Wang, Ting-Zhu Huang and Liang-Jian Deng

PLOS ONE, 2018, vol. 13, issue 1, 1-24

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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0182240

DOI: 10.1371/journal.pone.0182240

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