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) |
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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 | |
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 |
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Other
Other | 23rd European Signal Processing Conference, EUSIPCO 2015 |
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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