Many tasks previously performed by human analysts watching videos (live or recorded) are now being done by machine learning and artificial intelligence-based tools. Image enhancement techniques have been developed over decades to improve the quality of video for human operators. However, little work has been done to determine if this same approach can improve the efficacy of automated tools. In this paper, we briefly look at the improvement image enhancement can provide to common tasks such as detection, classification, and identification.
Video tracking of rocket launches inherently must be done from long range. Due to the high temperatures produced, cameras are often placed far from launch sites and their distance to the rocket increases as it is tracked through the flight. Consequently, the imagery collected is generally severely degraded by atmospheric turbulence. In this talk, we present our experience in enhancing commercial space flight videos. We will present the mission objectives, the unique challenges faced, and the solutions to overcome them.
Modern digital imaging systems are susceptible to degraded imagery because of atmospheric turbulence.
Notwithstanding significant improvements in resolution and speed, significant degradation of captured imagery still
hampers system designers and operators. Several techniques exist for mitigating the effects of the turbulence on
captured imagery, we will concentrate on the effects of Bi-Spectrum Speckle Averaging [1], [2] approach to image
enhancement, on a data-set captured in-conjunction with meteorological data.
Atmospheric turbulence degrades imagery by imparting scintillation and warping effects that can reduce the ability to
identify key features of the subjects. While visually, a human can intuitively understand the improvement that turbulence
mitigation techniques can offer in increasing visual information, this enhancement is rarely quantified in a meaningful
way. In this paper, we discuss methods for measuring the potential improvement on system performance video
enhancement algorithms can provide. To accomplish this, we explore two metrics. We use resolution targets to
determine the difference between imagery degraded by turbulence and that improved by atmospheric correction
techniques. By comparing line scans between the data before and after processing, it is possible to quantify the
additional information extracted. Advanced processing of this data can provide information about the effective
modulation transfer function (MTF) of the system when atmospheric effects are considered and removed, using this data
we compute a second metric, the relative improvement in Strehl ratio.
When capturing imagery over long distances, atmospheric turbulence often degrades the data, especially when observation 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 has 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 postprocessing workflows. EM Photonics has been developing image-processing-based turbulence mitigation technology since 2005. We will compare techniques from the literature with our commercially available, real-time, GPU-accelerated turbulence mitigation software. These comparisons will be made using real (not synthetic), experimentally obtained data for a variety of 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. Additionally, we will present a technique for quantitatively comparing turbulence mitigation algorithms using real images of radial resolution targets.
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