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

Journal Article · · Pattern Recognition Letters
 [1];  [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Naval Research Lab. (NRL), Washington, DC (United States)

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.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1604880
Report Number(s):
PNNL-SA-148925
Journal Information:
Pattern Recognition Letters, Vol. 133, Issue C; ISSN 0167-8655
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

Figures / Tables (4)