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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 9, 2016.
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.
W. Jino Hans, Lysiya Merlin.S, Venkateswaran N and Divya Priya T, “An Example-based Super-Resolution Algorithm for Multi-Spectral Remote Sensing Images” International Journal of Advanced Computer Science and Applications(IJACSA), 7(9), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070945
@article{Hans2016,
title = {An Example-based Super-Resolution Algorithm for Multi-Spectral Remote Sensing Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070945},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070945},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {9},
author = {W. Jino Hans and Lysiya Merlin.S and Venkateswaran N and Divya Priya T}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.