TY - GEN
T1 - PMMW image super resolution from compressed sensing observations
AU - Saafin, Wael
AU - Villena, Salvador
AU - Vega, Miguel
AU - Molina, Rafael
AU - Katsaggelos, Aggelos K.
N1 - Funding Information:
This paper has been supported by The European Union, Erasmus Mundus program, the Spanish Ministry of Economy and Competitiveness under project TIN2013-43880-R, the European Regional Development Fund (FEDER), the CEI BioTic at the Universidad de Granada, and the Department of Energy (DE-NA0002520)
PY - 2015/12/22
Y1 - 2015/12/22
N2 - 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.
AB - 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.
KW - Passive millimeter-wave
KW - compressive sensing
KW - image restoration
KW - super resolution
UR - http://www.scopus.com/inward/record.url?scp=84960523013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960523013&partnerID=8YFLogxK
U2 - 10.1109/EUSIPCO.2015.7362697
DO - 10.1109/EUSIPCO.2015.7362697
M3 - Conference contribution
AN - SCOPUS:84960523013
T3 - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
SP - 1815
EP - 1819
BT - 2015 23rd European Signal Processing Conference, EUSIPCO 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd European Signal Processing Conference, EUSIPCO 2015
Y2 - 31 August 2015 through 4 September 2015
ER -