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Tomography of Turbulence Strength Based on Scintillation Imaging

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13667))

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Abstract

Developed areas have plenty of artificial light sources. As the stars, they appear to twinkle, i.e., scintillate. This effect is caused by random turbulence. We leverage this phenomenon in order to reconstruct the spatial distribution of the turbulence strength (TS). Sensing is passive, using a multi-view camera setup in a city scale. The cameras sense the scintillation of light sources in the scene. The scintillation signal has a linear model of a line integral over the field of TS. Thus, the TS is recovered by linear tomography analysis. Scintillation offers measurements and TS recovery, which are more informative than tomography based on angle-of-arrival (projection distortion) statistics. We present the background and theory of the method. Then, we describe a large field experiment to demonstrate this idea, using distributed imagers. As far as we know, this work is the first to propose reconstruction of a TS horizontal field, using passive optical scintillation measurements.

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Notes

  1. 1.

    Note that tomography to sense properties of atmospheric scatterers is non-linear in the unknowns [21, 22, 30, 35].

  2. 2.

    From the illustration, despite beam spread caused by turbulence, if the camera lens is large enough, the lens gathers the light power as in non-turbulent air. This inhibits the scintillation signal, and termed aperture filtering. In practice, the aperture filters scintillation if \(D>5\) cm. In our work, this is negligible because \(D<5\) cm.

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Acknowledgments

We thank Uri, Ofra, Dor, Dvir and Inbar Shaul, Roi Ronen, Alon Preger, Haran Man, Vadim Holodovsky and Chanoch Kalifa for participating in the field experiment. Yoav Schechner is the Mark and Diane Seiden Chair in Science at the Technion. He is a Landau Fellow - supported by the Taub Foundation. His work was conducted in the Ollendorff Minerva Center. Minvera is funded through the BMBF. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (CloudCT, grant agreement No. 810370).

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Authors and Affiliations

  1. Viterbi Faculty of Electrical and Computers Engineering, Technion - Israel Institute of Technology, Haifa, Israel

    Nir Shaul & Yoav Y. Schechner

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  1. Nir Shaul
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  2. Yoav Y. Schechner
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Correspondence to Nir Shaul .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Shaul, N., Schechner, Y.Y. (2022). Tomography of Turbulence Strength Based on Scintillation Imaging. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-20071-7_28

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