Open AccessSingle image super-resolution based on approximated Heaviside functions and iterative refinement
Author(s) -
Xinyu Wang,
TingZhu Huang,
Liang-Jian Deng
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0182240
Subject(s) - heaviside step function , regularization (linguistics) , smoothness , image (mathematics) , algorithm , binary number , computer science , iterative method , image resolution , binary image , mathematics , image processing , artificial intelligence , mathematical analysis , arithmetic
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 l 1 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 l 1 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.