Abstract
An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images.
Original language | English |
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Pages (from-to) | 64-78 |
Number of pages | 15 |
Journal | Neural Networks |
Volume | 66 |
DOIs | |
Publication status | Published - 2015 Jun 1 |
Keywords
- Image super resolution
- Multi-frame super-resolution
- Sparse coding
ASJC Scopus subject areas
- Cognitive Neuroscience
- Artificial Intelligence