Back to Search
Start Over
Sparse representation with morphologic regularizations for single image super-resolution.
- Source :
-
Signal Processing . May2014, Vol. 98, p410-422. 13p. - Publication Year :
- 2014
-
Abstract
- Abstract: Due to the fact that natural images are inherently sparse in some domains, sparse representation has led to interesting results in image acquiring, representing, and compressing high-dimensional signals. Based on the experiences and learned priors in sparse domain from low and high resolution images, the typical ill-posed inverse problem of image super-resolution is effectively solved by the l 1-norm optimization techniques. However, how to reasonably combine the sparse representation theory and the feature of natural images is still a critical issue for performances improvements of image super-resolution algorithms. Considering the fact that the different morphologic features in natural images should be regularized by different constrains in sparse domain, in this paper we present a novel sparse representation algorithm with reasonable morphologic regularization for single image super-resolution. Extensive experimental results on various natural images validate the superiority of the proposed algorithm in terms of qualitative and quantitative performance. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01651684
- Volume :
- 98
- Database :
- Academic Search Index
- Journal :
- Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 94049623
- Full Text :
- https://doi.org/10.1016/j.sigpro.2013.11.032