Bayesian high resolution image reconstruction with incomplete multisensor low resolution systems. Mateos, J., Molina, R., & Katsaggelos, A. In 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., volume 3, pages III–705–8, 2003. IEEE. Paper doi abstract bibtex In this paper we consider the problem of reconstructing a high-resolution image from an incomplete set of undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the calculation of the maximum a posteriori (MAP) estimate of the high resolution image given the low resolution observed images. We also examine the role played by the prior model when an incomplete set of low resolution images is used. Finally, the proposed method is tested on real and synthetic images.
@inproceedings{Javier2003,
abstract = {In this paper we consider the problem of reconstructing a high-resolution image from an incomplete set of undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the calculation of the maximum a posteriori (MAP) estimate of the high resolution image given the low resolution observed images. We also examine the role played by the prior model when an incomplete set of low resolution images is used. Finally, the proposed method is tested on real and synthetic images.},
author = {Mateos, Javier and Molina, Rafael and Katsaggelos, A.K.},
booktitle = {2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).},
doi = {10.1109/ICASSP.2003.1199572},
isbn = {0-7803-7663-3},
issn = {15206149},
pages = {III--705--8},
publisher = {IEEE},
title = {{Bayesian high resolution image reconstruction with incomplete multisensor low resolution systems}},
url = {http://ieeexplore.ieee.org/document/1199572/},
volume = {3},
year = {2003}
}
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