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Single image super-resolution based on approximated Heaviside functions and iterative refinement.
- Source :
- PLoS ONE; 1/12/2018, Vol. 13 Issue 1, p1-24, 24p
- Publication Year :
- 2018
-
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 l<subscript>1</subscript> 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<subscript>1</subscript> 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 127327922
- Full Text :
- https://doi.org/10.1371/journal.pone.0182240