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  4. Performance assessment of reconstruction algorithms for compressed sensing threat detection applications
 
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2020
Conference Paper
Title

Performance assessment of reconstruction algorithms for compressed sensing threat detection applications

Abstract
The novel Compressed Sensing (CS) imaging technique possesses fundamental differences and in special cases outstanding advantages compared to conventional imaging. The crucial requirement is the validity of the ""sparsity"" assumption. Fulfilling this requirement enables the reconstruction of a high-resolution image/signal from very few low-resolution measurements. An excellent example of sparse signals are solar blind UV imaging applications. The image of a hot exhaust plume in front of the dark background is spatially sparse. Hence, a solar blind threat detection system may be considered using a single-pixel-camera equipped with the Compressed Sensing approach. This simple and cheap setup with a spatial light modulator and a single-pixel-detector measures only the brightness of a modulated target scene. It is necessary to reconstruct the original image from the obtained compressed data. Many reconstruction algorithms have been proposed and implemented for various CS applications. However, the demands for a CS threat warning system are very specific. Therefore, we built up a library of common reconstruction algorithms and studied their performance in terms of different image quality assessment metrics. In our investigations we utilized exemplary test scenes as well as a real dataset.
Author(s)
Limbach, Jürgen  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Eisele, Christian  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Mainwork
Emerging Imaging and Sensing Technologies for Security and Defence V, and Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III  
Conference
Conference "Emerging Imaging and Sensing Technologies for Security and Defence" 2020  
Conference "Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence" 2020  
DOI
10.1117/12.2572598
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Keyword(s)
  • compressed sensing

  • Computational Imaging

  • missile approach warning system

  • single-pixel-camera

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