Incorporating the effects of objects in an approximate model of light transport in scattering media

Abstract

A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.

Event-based sensing for the detection of modulated signals in degraded visual environments

“…Because of the effects of surface tension, water droplets suspended in air are approximately spherical, and in a nonturbulent environment it can be assumed they are far enough apart from one another that Mie scattering theory can be applied 3,5,8,15,22 .…”

“…We employ a MATLAB code from Mätzler 23 to perform these calculations. As these scattering and absorption effects compound, optical signal decreases in intensity and is spatially blurred 2,3,7,8,15,24 . These effects can be integrated for a bulk aerosol through understanding the microphysical characteristics of the aerosol 3,5,7,8,15,22,25 (e.g., droplet size distribution and number density).…”

“…As these scattering and absorption effects compound, optical signal decreases in intensity and is spatially blurred 2,3,7,8,15,24 . These effects can be integrated for a bulk aerosol through understanding the microphysical characteristics of the aerosol 3,5,7,8,15,22,25 (e.g., droplet size distribution and number density). The bulk aerosol scattering coefficient, 𝜇 𝑠 (calculated in Equation 1, Section 3.1) is a useful metric to compare the performance of optical systems in DVEs as it directly relates the optical properties of the DVE to its microphysical properties, thus allowing for the comparison of optical sensing systems via a relevant, physics-informed metric.…”

“…Ultimately, these effects can have significant impact on sensing infrastructure and cannot be easily overcome. As such, there is continued work to develop and seek out novel solutions which may improve sensing through DVEs [2][3][4][5][6][7][8] . In this work we focus on assessing the potential use of event-based sensors (EBSs) in a DVE caused by an experimentally generated atmospheric aerosol analogue.…”

“…Below, we provide some high-level background relevant to the discussions contained within the manuscript. We would like to specifically draw attention to the fact that at present we are comparing our current work and analysis to previous work performed in this space using framing cameras 2,3,5,[7][8][9][10][11][12][13][14][15][16][17][18] . These framing datasets were collected in a variety of different DVEs, some experimentally generated and others naturally occurring.…”