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

Bing Ouyang; Weilin W. Hou; Frank M. Caimi; Fraser R. Dalgleish; Anni K. Vuorenkoski; Cuiling Gong

doi:10.1117/1.JRS.11.032407

3 May 2017 Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application

Bing Ouyang, Weilin W. Hou, Frank M. Caimi, Fraser R. Dalgleish, Anni K. Vuorenkoski, Cuiling Gong

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 3, 032407 (May 2017). https://doi.org/10.1117/1.JRS.11.032407

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.

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Bing Ouyang, Weilin W. Hou, Frank M. Caimi, Fraser R. Dalgleish, Anni K. Vuorenkoski, and Cuiling Gong "Integrating dynamic and distributed compressive sensing techniques to enhance image quality of the compressive line sensing system for unmanned aerial vehicles application," Journal of Applied Remote Sensing 11(3), 032407 (3 May 2017). https://doi.org/10.1117/1.JRS.11.032407

Received: 2 December 2016; Accepted: 6 April 2017; Published: 3 May 2017

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