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Volume: 32 | Article ID: art00012
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Imaging through Scattering Media with a Learning Based Prior
  DOI :  10.2352/ISSN.2470-1173.2020.14.COIMG-306  Published OnlineJanuary 2020
Abstract

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

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Florian Schiffers, Lionel Fiske, Pablo Ruiz, Aggelos K. Katsaggelos, Oliver Cossairt, "Imaging through Scattering Media with a Learning Based Priorin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVIII,  2020,  pp 306-1 - 306-6,  https://doi.org/10.2352/ISSN.2470-1173.2020.14.COIMG-306

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