PMMW image super resolution from compressed sensing observations. Saafin, W., Villena, S., Vega, M., Molina, R., & Katsaggelos, A. K. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 1815–1819, aug, 2015. IEEE. ![PMMW image super resolution from compressed sensing observations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this paper we propose a novel optimization framework to obtain High Resolution (HR) Passive Millimeter Wave (P-MMW) images from multiple Low Resolution (LR) observations captured using a simulated Compressed Sensing (CS) imaging system. The proposed CS Super Resolution (CSS-R) approach combines existing CS reconstruction algorithms with the use of Super Gaussian (SG) regularization terms on the image to be reconstructed, smoothness constraints on the registration parameters to be estimated and the use of the Alternate Direction Methods of Multipliers (ADMM) to link the CS and SR problems. The image estimation subproblem is solved using Majorization-Minimization (MM), registration is tackled minimizing a quadratic function and CS reconstruction is approached as an l1-minimization problem subject to a quadratic constraint. The performed experiments, on simulated and real PMMW observations, validate the used approach.
@inproceedings{Wael2015,
abstract = {In this paper we propose a novel optimization framework to obtain High Resolution (HR) Passive Millimeter Wave (P-MMW) images from multiple Low Resolution (LR) observations captured using a simulated Compressed Sensing (CS) imaging system. The proposed CS Super Resolution (CSS-R) approach combines existing CS reconstruction algorithms with the use of Super Gaussian (SG) regularization terms on the image to be reconstructed, smoothness constraints on the registration parameters to be estimated and the use of the Alternate Direction Methods of Multipliers (ADMM) to link the CS and SR problems. The image estimation subproblem is solved using Majorization-Minimization (MM), registration is tackled minimizing a quadratic function and CS reconstruction is approached as an l1-minimization problem subject to a quadratic constraint. The performed experiments, on simulated and real PMMW observations, validate the used approach.},
author = {Saafin, Wael and Villena, Salvador and Vega, Miguel and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
doi = {10.1109/EUSIPCO.2015.7362697},
isbn = {978-0-9928-6263-3},
keywords = {Passive millimeter-wave,compressive sensing,image restoration,super resolution},
month = {aug},
pages = {1815--1819},
publisher = {IEEE},
title = {{PMMW image super resolution from compressed sensing observations}},
url = {http://ieeexplore.ieee.org/document/7362697/},
year = {2015}
}
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