Towards computational imaging for intelligence in highly scattering aerosols

Brian Z. Bentz; Brian J. Redman; John D. van der Laan; Karl Westlake; Andrew Glen; Andres L. Sanchez; Jeremy B. Wright

doi:10.1117/12.2558820

4 May 2020 Towards computational imaging for intelligence in highly scattering aerosols

Brian Z. Bentz, Brian J. Redman, John D. van der Laan, Karl Westlake, Andrew Glen, Andres L. Sanchez, Jeremy B. Wright

Author Affiliations +

Proceedings Volume 11424, Situation Awareness in Degraded Environments 2020; 1142405 (2020) https://doi.org/10.1117/12.2558820
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

This communication reports progress towards the development of computational sensing and imaging methods that utilize highly scattered light to extract information at greater depths in degraded visual environments like fog for improved situational awareness. As light propagates through fog, information is lost due to random scattering and absorption by micrometer sized water droplets. Computational diffuse optical imaging shows promise for interpreting the detected scattered light, enabling greater depth penetration than current methods. Developing this capability requires verification and validation of diffusion models of light propagation in fog. We report models that were developed and compared to experimental data captured at the Sandia National Laboratory Fog Chamber facility. The diffusion approximation to the radiative transfer equation was found to predict light propagation in fog under the appropriate conditions.

Conference Presentation

Citation Download Citation

Brian Z. Bentz, Brian J. Redman, John D. van der Laan, Karl Westlake, Andrew Glen, Andres L. Sanchez, and Jeremy B. Wright "Towards computational imaging for intelligence in highly scattering aerosols", Proc. SPIE 11424, Situation Awareness in Degraded Environments 2020, 1142405 (4 May 2020); https://doi.org/10.1117/12.2558820

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