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Journal of Applied Remote Sensing

Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application
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Paper Abstract

The compressive line sensing imaging system adopts distributed compressive sensing (CS) to acquire data and reconstruct images. Dynamic CS uses Bayesian inference to capture the correlated nature of the adjacent lines. An image reconstruction technique that incorporates dynamic CS in the distributed CS framework was developed to improve the quality of reconstructed images. The effectiveness of the technique was validated using experimental data acquired in an underwater imaging test facility. Results that demonstrate contrast and resolution improvements will be presented. The improved efficiency is desirable for unmanned aerial vehicles conducting long-duration missions.

Paper Details

Date Published: 3 May 2017
PDF: 14 pages
J. Appl. Rem. Sens. 11(3) 032407 doi: 10.1117/1.JRS.11.032407
Published in: Journal of Applied Remote Sensing Volume 11, Issue 3
Show Author Affiliations
Bing Ouyang, Florida Atlantic Univ. (United States)
Weilin W. Hou, U.S. Naval Research Lab. (United States)
Frank M. Caimi, Florida Atlantic Univ. (United States)
Fraser R. Dalgleish, Florida Atlantic Univ. (United States)
Anni K. Vuorenkoski, Florida Atlantic Univ. (United States)
Cuiling Gong, Texas Christian Univ. (United States)

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