Compressive sensing super resolution from multiple observations with application to passive millimeter wave images
In this work we propose a novel framework to obtain high resolution images from compressed sensing imaging systems capturing multiple low resolution images of the same scene. The proposed approach of Compressed Sensing Super Resolution (CSSR), combines existing compressed sensing reconstruction algo...
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| Published in | Digital signal processing Vol. 50; no. C; pp. 180 - 190 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.03.2016
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1051-2004 1095-4333 |
| DOI | 10.1016/j.dsp.2015.12.005 |
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| Abstract | In this work we propose a novel framework to obtain high resolution images from compressed sensing imaging systems capturing multiple low resolution images of the same scene. The proposed approach of Compressed Sensing Super Resolution (CSSR), combines existing compressed sensing reconstruction algorithms with a low-resolution to high-resolution approach based on the use of a super Gaussian regularization term. The reconstruction alternates between compressed sensing reconstruction and super resolution reconstruction, including registration parameter estimation. The image estimation subproblem is solved using majorization-minimization while the compressed sensing reconstruction becomes an l1-minimization subject to a quadratic constraint. The performed experiments on grayscale and synthetically compressed real millimeter wave images, demonstrate the capability of the proposed framework to provide very good quality super resolved images from multiple low resolution compressed acquisitions. |
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| AbstractList | In this work we propose a novel framework to obtain high resolution images from compressed sensing imaging systems capturing multiple low resolution images of the same scene. The proposed approach of Compressed Sensing Super Resolution (CSSR), combines existing compressed sensing reconstruction algorithms with a low-resolution to high-resolution approach based on the use of a super Gaussian regularization term. The reconstruction alternates between compressed sensing reconstruction and super resolution reconstruction, including registration parameter estimation. The image estimation subproblem is solved using majorization-minimization while the compressed sensing reconstruction becomes an -minimization subject to a quadratic constraint. The performed experiments on grayscale and synthetically compressed real millimeter wave images, demonstrate the capability of the proposed framework to provide very good quality super resolved images from multiple low resolution compressed acquisitions. In this work we propose a novel framework to obtain high resolution images from compressed sensing imaging systems capturing multiple low resolution images of the same scene. The proposed approach of Compressed Sensing Super Resolution (CSSR), combines existing compressed sensing reconstruction algorithms with a low-resolution to high-resolution approach based on the use of a super Gaussian regularization term. The reconstruction alternates between compressed sensing reconstruction and super resolution reconstruction, including registration parameter estimation. The image estimation subproblem is solved using majorization-minimization while the compressed sensing reconstruction becomes an l1-minimization subject to a quadratic constraint. The performed experiments on grayscale and synthetically compressed real millimeter wave images, demonstrate the capability of the proposed framework to provide very good quality super resolved images from multiple low resolution compressed acquisitions. |
| Author | Villena, Salvador AlSaafin, Wael Katsaggelos, Aggelos K. Vega, Miguel Molina, Rafael |
| Author_xml | – sequence: 1 givenname: Wael orcidid: 0000-0002-0824-5753 surname: AlSaafin fullname: AlSaafin, Wael email: waelsaafin@hotmail.com organization: Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain – sequence: 2 givenname: Salvador surname: Villena fullname: Villena, Salvador email: svillena@ugr.es organization: Dept. of Languages and Information Systems, University of Granada, Spain – sequence: 3 givenname: Miguel surname: Vega fullname: Vega, Miguel email: mvega@ugr.es organization: Dept. of Languages and Information Systems, University of Granada, Spain – sequence: 4 givenname: Rafael surname: Molina fullname: Molina, Rafael email: rms@decsai.ugr.es organization: Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain – sequence: 5 givenname: Aggelos K. surname: Katsaggelos fullname: Katsaggelos, Aggelos K. email: aggk@eecs.northwestern.edu organization: Dept. of Electrical Engineering and Computer Science, Northwestern University, USA |
| BackLink | https://www.osti.gov/biblio/1346578$$D View this record in Osti.gov |
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| Cites_doi | 10.1117/1.JEI.22.4.043003 10.1063/1.2989126 10.1109/TIP.2004.834669 10.1002/nla.783 10.1109/JSTARS.2012.2189555 10.1117/12.600722 10.1109/MSP.2007.914730 10.1109/TIP.2007.908074 10.1109/TIP.2013.2266100 10.4028/www.scientific.net/AMM.721.779 10.1016/j.dsp.2012.10.002 10.1109/MSP.2007.914731 10.1364/OE.21.028583 10.1561/2200000016 10.1109/MSP.2005.1406480 10.1117/1.OE.51.9.091614 |
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| Snippet | In this work we propose a novel framework to obtain high resolution images from compressed sensing imaging systems capturing multiple low resolution images of... |
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| SubjectTerms | Algorithms Compressed sensing Detection Gaussian Image quality Image reconstruction Millimeter waves Passive millimeter wave images Reconstruction Regularization Super resolution |
| Title | Compressive sensing super resolution from multiple observations with application to passive millimeter wave images |
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