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Example-based image super-resolution with class-specific predictors
- Source :
-
Journal of Visual Communication & Image Representation . Jul2009, Vol. 20 Issue 5, p312-322. 11p. - Publication Year :
- 2009
-
Abstract
- Abstract: Example-based super-resolution is a promising approach to solving the image super-resolution problem. However, the learning process can be slow and prediction can be inaccurate. In this paper, we present a novel learning-based algorithm for image super-resolution to improve the computational speed and prediction accuracy. Our new method classifies image patches into several classes, for each class, a class-specific predictor is designed. A class-specific predictor takes a low-resolution image patch as input and predicts a corresponding high-resolution patch as output. The performances of the class-specific predictors are evaluated using different datasets formed by face images and natural-scene images. We present experimental results which demonstrate that the new method provides improved performances over existing methods. [Copyright &y& Elsevier]
- Subjects :
- *OPTICAL resolution
*LEARNING
*ALGORITHMS
*IMAGE
Subjects
Details
- Language :
- English
- ISSN :
- 10473203
- Volume :
- 20
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Visual Communication & Image Representation
- Publication Type :
- Academic Journal
- Accession number :
- 40120656
- Full Text :
- https://doi.org/10.1016/j.jvcir.2009.03.008