An Example-based Super-Resolution Algorithm for Multi-Spectral Remote Sensing Images

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}
}