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14 May 2019 A data-constrained algorithm for the emulation of long-range turbulence-degraded video
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Atmospheric turbulence can cause significant image quality degradation in long-range, ground-to-ground imagery. There is recent interest in characterizing the performance of machine learning algorithms for long-range imaging applications. However, such as task requires a databases of realistic turbulence-degraded imagery. Modeling and simulation provides a reliable, repeatable means of generating long-range data at a substantial cost-savings compared to live field collections. We present updates to the Night Vision Electronic Sensors Directorate (NVESD) Turbulence Simulation algorithm that simulates the effect of turbulence on imagery by imposing realistic blur and distortion on pristine input imagery for a given range, turbulence condition, and optical parameters. Key improvements to the model are: (1) the incorporation of the exact short-exposure atmospheric modulation transfer function into the blurring routine; (2) a random walk algorithm that generates blur and distortion statistics on-the-fly at the characteristic frequency of turbulence degradations. The algorithm is fast and lightweight, computationally-speaking, so as to be scalable to high-performance computing. We perform a qualitative assessment of the results with real field imagery, as well as a quantitative comparison using the structural similarity metric (SSIM).
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Kevin J. Miller, Bradley Preece, Todd W. Du Bosq, and Kevin R. Leonard "A data-constrained algorithm for the emulation of long-range turbulence-degraded video", Proc. SPIE 11001, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXX, 110010J (14 May 2019);

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