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Title: Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors

Abstract

This work exploits a connection between optimal transport theory and the physics of image propagation to yield a locally low-dimensional model of turbulence-corrupted imagery. Optimal transport produces an invertible, pixel-wise linear trajectories to approximate the globally nonlinear turbulence between a clean and turbulence corrupted image pair. We use the low-dimensional model to fit subsets of the optimal transport vector fields and stitch the local models into a surrogate for the global map to be used for image cleaning. Experiments are performed on laboratory generated data of beam propagation using different values of the Fried parameter (a scale measuring turbulence coherence) as well as a toy data set. The results suggest this is a fruitful direction, and first step, towards using multiple realizations of turbulence corrupted images to learn a blind surrogate for the optimal transport vector field for image cleaning.

Authors:
 [1];  [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Naval Research Lab. (NRL), Washington, DC (United States)
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1604880
Report Number(s):
PNNL-SA-148925
Journal ID: ISSN 0167-8655
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Pattern Recognition Letters
Additional Journal Information:
Journal Volume: 133; Journal Issue: C; Journal ID: ISSN 0167-8655
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Turbulence; Optimal transport; Velocity fields; Geometric optics

Citation Formats

Emerson, Tegan H., and Nichols, Jonathan. Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors. United States: N. p., 2019. Web. doi:10.1016/j.patrec.2019.10.023.
Emerson, Tegan H., & Nichols, Jonathan. Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors. United States. https://doi.org/10.1016/j.patrec.2019.10.023
Emerson, Tegan H., and Nichols, Jonathan. Mon . "Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors". United States. https://doi.org/10.1016/j.patrec.2019.10.023. https://www.osti.gov/servlets/purl/1604880.
@article{osti_1604880,
title = {Fitting local, low-dimensional parameterizations of optical turbulence modeled from optimal transport velocity vectors},
author = {Emerson, Tegan H. and Nichols, Jonathan},
abstractNote = {This work exploits a connection between optimal transport theory and the physics of image propagation to yield a locally low-dimensional model of turbulence-corrupted imagery. Optimal transport produces an invertible, pixel-wise linear trajectories to approximate the globally nonlinear turbulence between a clean and turbulence corrupted image pair. We use the low-dimensional model to fit subsets of the optimal transport vector fields and stitch the local models into a surrogate for the global map to be used for image cleaning. Experiments are performed on laboratory generated data of beam propagation using different values of the Fried parameter (a scale measuring turbulence coherence) as well as a toy data set. The results suggest this is a fruitful direction, and first step, towards using multiple realizations of turbulence corrupted images to learn a blind surrogate for the optimal transport vector field for image cleaning.},
doi = {10.1016/j.patrec.2019.10.023},
journal = {Pattern Recognition Letters},
number = C,
volume = 133,
place = {United States},
year = {2019},
month = {10}
}

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Cited by: 2 works
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Figures / Tables:

Figure 1 Figure 1: (left) Turbulence-free image of a coherent beam, (middle) the optimal transport velocity vector field, and (right) the turbulence-corrupted image of the beam.

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