PMMW image super resolution from compressed sensing observations

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1815-1819
Number of pages5
ISBN (Electronic)9780992862633
DOIs
StatePublished - Dec 22 2015
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: Aug 31 2015Sep 4 2015

Publication series

Name2015 23rd European Signal Processing Conference, EUSIPCO 2015

Other

Other23rd European Signal Processing Conference, EUSIPCO 2015
CountryFrance
CityNice
Period8/31/159/4/15

Keywords

  • Passive millimeter-wave
  • compressive sensing
  • image restoration
  • super resolution

ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing

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