Abstract

In long-range imaging applications, anisoplanatic atmospheric optical turbulence imparts spatially- and temporally varying blur and geometric distortions in acquired imagery. The ability to distinguish true scene motion from turbulence warping is important for many image-processing and analysis tasks. The authors present a scene-motion detection algorithm specifically designed to operate in the presence of anisoplanatic optical turbulence. The method models intensity fluctuations in each pixel with a Gaussian mixture model (GMM). The GMM uses knowledge of the turbulence tilt-variance statistics. We provide both quantitative and qualitative performance analyses and compare the proposed method to several state-of-the art algorithms. The image data are generated with an anisoplanatic numerical wave-propagation simulator that allows us to have motion truth. The subject technique outperforms the benchmark methods in our study.

© 2021 Optical Society of America

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