Special Section on Recent Advances in Geophysical Sensing of the Ocean: Remote and In Situ Methods

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

[+] Author Affiliations
Bing Ouyang, Frank M. Caimi, Fraser R. Dalgleish, Anni K. Vuorenkoski

Florida Atlantic University, Harbor Branch Oceanographic Institute, Fort Pierce, Florida, United States

Weilin Hou

Naval Research Laboratory, Stennis Space Center, Mississippi, United States

Cuiling Gong

Texas Christian University, Department of Engineering, Fort Worth, Texas, United States

J. Appl. Remote Sens. 11(3), 032407 (May 03, 2017). doi:10.1117/1.JRS.11.032407
History: Received December 2, 2016; Accepted April 6, 2017
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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.

© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Bing Ouyang ; Weilin Hou ; Frank M. Caimi ; Fraser R. Dalgleish ; Anni K. Vuorenkoski, et al.
"Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application", J. Appl. Remote Sens. 11(3), 032407 (May 03, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.032407


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