An overview of turbulence compensation

Schutte, Klamer; Eekeren, Adam W. M. van; Dijk, J. C. van; Schwering, Piet B. W.; Iersel, M. van; Doelman, Niek

doi:10.1117/12.981942

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Turbulence (heat haze) cause image blur-related degradation [35,36,37,38] that could be represented as the turbulence component of the atmospheric MTF. This approach is not exact for atmospheric modeling, but provides a first-order approximation [91].…”

Section: Thermal Imager Range Predictionsmentioning

confidence: 99%

“…Experimental field trials [34] are poorly covered in the literature due to the confidentiality of data. The influence of atmospheric scattering and turbulence [35,36,37,38] are studied mainly regarding laser systems, but all data could be used for thermal imagers. Target signature studies and their corresponding environmental influences [39,40,41,42] contributed to a better understanding of the thermal imager range.…”

Section: Introductionmentioning

confidence: 99%

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Thermal Imager Range: Predictions, Expectations, and Reality

Peric¹,

Livada²,

Peric³

et al. 2019

Sensors

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Imaging system range defines the maximal distance at which a selected object can be seen and perceived following surveillance task perception criteria. Thermal imagers play a key role in long-range surveillance systems due to the ability to form images during the day or night and in adverse weather conditions. The thermal imager range depends on imager design parameters, scene and transmission path properties. Imager range prediction is supported by theoretical models that provide the ability to check range performance, compare range performances for different systems, extend range prediction in field conditions, and support laboratory measurements related to range. A condensed review of the theoretical model’s genesis and capabilities is presented. We applied model-based performance calculation for several thermal imagers used in our long-range surveillance systems and compared the results with laboratory performance measurement results with the intention of providing the range prediction in selected field conditions. The key objective of the paper is to provide users with reliable data regarding expectations during a field mission.

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Section: Thermal Imager Range Predictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thermal Imager Range: Predictions, Expectations, and Reality

Peric¹,

Livada²,

Peric³

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…[4][5][6] A variety of turbulence mitigation (TM) methods have been developed to address this issue by processing multiple short exposure images. [7][8][9] There is an important connection between optical turbulence and the undersampling problem. With heavy turbulence, the blurring acts as an anti-aliasing low-pass filter that lowers the effective cut-off frequency of the optical system and limits or prevents aliasing.…”

Section: Introductionmentioning

confidence: 99%

“…Sensor and algorithm parameters are listed inTable 5.Optical Engineering083103-14 August 2019 • Vol. 58(8)…”

mentioning

confidence: 99%

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

et al. 2019

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An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this task. We propose a variation of the FIF SR method that has a fusion parameter that allows it to transition from traditional diffraction-limited SR to pure TM with no SR as well as a continuum in between. This fusion parameter balances subpixel resolution, needed for SR, with the amount of temporal averaging, needed for TM and noise reduction. In addition, we develop a model of the interpolation blurring that results from the fusion process, as a function of this tuning parameter. The blurring model is then incorporated into the overall degradation model that is addressed in the restoration step of the FIF SR method. This innovation benefits the FIF SR method in all applications. We present a number of experimental results to demonstrate the efficacy of the FIF SR method in different levels of turbulence. Simulated imagery with known ground truth is used for a detailed quantitative analysis. Three real infrared image sequences are also used. Two of these include bar targets that allow for a quantitative resolution enhancement assessment.

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Quantitative evaluation of turbulence compensation

et al. 2013

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A well-known phenomena that diminishes the recognition range in infrared imagery is atmospheric turbulence. In literature many methods are described that try to compensate for the distortions caused by atmospheric turbulence. Most of these methods use a global processing approach in which they assume a global shift and a uniform blurring in all frames. Because the effects of atmospheric turbulence are often spatial and temporal varying, we presented previous year a turbulence compensation method that performs local processing leading to excellent results. In this paper an improvement of this method is presented which uses a temporal moving reference frame in order to be capable of processing imagery containing moving objects as well as blur estimation to obtain adaptive deconvolution. Furthermore our method is evaluated in a quantitative way, which will give a good insight in which components of our method contribute to the obtained visual improvements.

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An overview of turbulence compensation

Cited by 4 publications

References 24 publications

Thermal Imager Range: Predictions, Expectations, and Reality

Thermal Imager Range: Predictions, Expectations, and Reality

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

Quantitative evaluation of turbulence compensation

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