Compressive sensing super resolution from multiple observations with application to passive millimeter wave images

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

doi:10.1016/j.dsp.2015.12.005

Compressive sensing super resolution from multiple observations with application to passive millimeter wave images

Journal Article · Tue Jan 05 00:00:00 EST 2016 · Digital Signal Processing

DOI:https://doi.org/10.1016/j.dsp.2015.12.005· OSTI ID:1487694

AlSaafin, Wael ^[1]; Villena, Salvador ^[2]; Vega, Miguel ^[2]; Molina, Rafael ^[2]; ^[3]

University of Granada, Granada (Spain); Northwestern University
University of Granada, Granada (Spain)
Northwestern University, Evanston, IL (United States)

In this study 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.

View Accepted Manuscript (DOE)

Research Organization:: Northwestern University, Evanston, IL (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002520

OSTI ID:: 1487694

Alternate ID(s):: OSTI ID: 1346578

Journal Information:: Digital Signal Processing, Journal Name: Digital Signal Processing Journal Issue: C Vol. 50; ISSN 1051-2004

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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42 ENGINEERING
97 MATHEMATICS AND COMPUTING
compressed sensing
image reconstruction
passive millimeter wave images
super resolution

Compressive sensing super resolution from multiple observations with application to passive millimeter wave images

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References (16)

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