An Example-based Super-Resolution Algorithm for Multi-Spectral Remote Sensing Images
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 9
Abstract
This paper proposes an example-based super-resolution algorithm for multi-spectral remote sensing images. The underlying idea of this algorithm is to learn a matrix-based implicit prior from a set of high-resolution training examples to model the relation between LR and HR images. The matrix-based implicit prior is learned as a regression operator using conjugate decent method. The direct relation between LR and HR image is obtained from the regression operator and it is used to super-resolve low-resolution multi-spectral remote sensing images. A detailed performance evaluation is carried out to validate the strength of the proposed algorithm.
Authors and Affiliations
W. Hans, Lysiya Merlin. S, Venkateswaran N, Divya Priya T
Keywords
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- EP ID EP96386
- DOI 10.14569/IJACSA.2016.070945
- Views 103
- Downloads 0
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