Comparison of turbulence mitigation algorithms

Stephen Kozacik; Aaron Paolini; James Bonnett; Eric Kelmelis

doi:10.1117/12.2225617

2 June 2016 Comparison of turbulence mitigation algorithms

Stephen Kozacik, Aaron Paolini, James Bonnett, Eric Kelmelis

Proceedings Volume 9846, Long-Range Imaging; 98460B (2016) https://doi.org/10.1117/12.2225617
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

When capturing image data over long distances (0.5 km and above), images are often degraded by atmospheric turbulence, especially when imaging paths are close to the ground or in hot environments. These issues manifest as time-varying scintillation and warping effects that decrease the effective resolution of the sensor and reduce actionable intelligence. In recent years, several image processing approaches to turbulence mitigation have shown promise. Each of these algorithms have different computational requirements, usability demands, and degrees of independence from camera sensors. They also produce different degrees of enhancement when applied to turbulent imagery. Additionally, some of these algorithms are applicable to real-time operational scenarios while others may only be suitable for post-processing workflows. EM Photonics has been developing image-processing-based turbulence mitigation technology since 2005 as a part of our ATCOM [1] image processing suite. In this paper we will compare techniques from the literature with our commercially available real-time GPU accelerated turbulence mitigation software suite, as well as in-house research algorithms. These comparisons will be made using real, experimentally-obtained data for a variety of different conditions, including varying optical hardware, imaging range, subjects, and turbulence conditions. Comparison metrics will include image quality, video latency, computational complexity, and potential for real-time operation.

Citation Download Citation

Stephen Kozacik, Aaron Paolini, James Bonnett, and Eric Kelmelis "Comparison of turbulence mitigation algorithms", Proc. SPIE 9846, Long-Range Imaging, 98460B (2 June 2016); https://doi.org/10.1117/12.2225617

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KEYWORDS

Image fusion

Turbulence

Reconstruction algorithms

Speckle

Deconvolution

Image processing

Cameras

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