PMMW image super resolution from compressed sensing observations

Wael Saafin; Salvador Villena; Miguel Vega; Rafael Molina; Aggelos K. Katsaggelos

doi:10.1109/EUSIPCO.2015.7362697

PMMW image super resolution from compressed sensing observations

Wael Saafin, Salvador Villena, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

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.

Original language	English (US)
Title of host publication	2015 23rd European Signal Processing Conference, EUSIPCO 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1815-1819
Number of pages	5
ISBN (Electronic)	9780992862633
DOIs	https://doi.org/10.1109/EUSIPCO.2015.7362697
State	Published - Dec 22 2015
Event	23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France Duration: Aug 31 2015 → Sep 4 2015

Publication series

Name	2015 23rd European Signal Processing Conference, EUSIPCO 2015

Other

Other	23rd European Signal Processing Conference, EUSIPCO 2015
Country/Territory	France
City	Nice
Period	8/31/15 → 9/4/15

Funding

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)

Keywords

Passive millimeter-wave
compressive sensing
image restoration
super resolution

ASJC Scopus subject areas

Media Technology
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/EUSIPCO.2015.7362697

Cite this

Saafin, W., Villena, S., Vega, M., Molina, R., & Katsaggelos, A. K. (2015). PMMW image super resolution from compressed sensing observations. In 2015 23rd European Signal Processing Conference, EUSIPCO 2015 (pp. 1815-1819). Article 7362697 (2015 23rd European Signal Processing Conference, EUSIPCO 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EUSIPCO.2015.7362697

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title = "PMMW image super resolution from compressed sensing observations",

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.",

keywords = "Passive millimeter-wave, compressive sensing, image restoration, super resolution",

author = "Wael Saafin and Salvador Villena and Miguel Vega and Rafael Molina and Katsaggelos, \{Aggelos K.\}",

note = "Publisher Copyright: {\textcopyright} 2015 EURASIP.; 23rd European Signal Processing Conference, EUSIPCO 2015 ; Conference date: 31-08-2015 Through 04-09-2015",

year = "2015",

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doi = "10.1109/EUSIPCO.2015.7362697",

language = "English (US)",

series = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Saafin, W, Villena, S, Vega, M, Molina, R & Katsaggelos, AK 2015, PMMW image super resolution from compressed sensing observations. in 2015 23rd European Signal Processing Conference, EUSIPCO 2015., 7362697, 2015 23rd European Signal Processing Conference, EUSIPCO 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1815-1819, 23rd European Signal Processing Conference, EUSIPCO 2015, Nice, France, 8/31/15. https://doi.org/10.1109/EUSIPCO.2015.7362697

PMMW image super resolution from compressed sensing observations. / Saafin, Wael; Villena, Salvador; Vega, Miguel et al.
2015 23rd European Signal Processing Conference, EUSIPCO 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1815-1819 7362697 (2015 23rd European Signal Processing Conference, EUSIPCO 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Saafin, Wael

AU - Villena, Salvador

AU - Vega, Miguel

AU - Molina, Rafael

AU - Katsaggelos, Aggelos K.

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.

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PMMW image super resolution from compressed sensing observations

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