Simulation of a laser range-gated SWIR imaging system in weak turbulence conditions

Oxford, David; Espinola, Richard L.

doi:10.1117/12.883648

Cited by 4 publications

(8 citation statements)

References 11 publications

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“…Step2. The spatial power spectral density function S β (f ) provided in reference [20]- [22], which is consistent with the actual imaging equipment and can reflect image distortion, is expressed in the form of S β (f ) = f −b , b = 3 [20]- [22]. This function describes the spatial correlation between geometric distortion variables in different regions of the image caused by atmospheric turbulence.…”

Section: Appendix Appendix Amentioning

confidence: 90%

“…Here, we combine the physical imaging models in turbulence with the image processing algorithms, namely the image interpolation and image convolution methods. For the classic isotropic Kolmogorov turbulence, the turbulence-degraded image can be simulated with the following formula [20]- [22]:…”

Section: A Imaging Mechanism In Atmospheric Turbulence Mediamentioning

confidence: 99%

“…Step 3. The complex Gauss random matrix generated in step 1 is used to filter S β (f ), and a complex random field in frequency domain, which can reflect both the spatial correlation characteristics of image distortion and the random variation characteristics of atmospheric turbulence, can be expressed as [20]- [22]:…”

Section: Appendix Appendix Amentioning

confidence: 99%

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Accurate Semantic Segmentation in Turbulence Media

Cui

Zhang

2019

IEEE Access

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Accurate image semantic segmentation in atmosphere turbulence conditions is challenging due to the severe degradation effects introduced by the random refractive-index fluctuations of atmosphere. In this paper, we present an end-to-end trainable methodology for turbulence-degraded image semantic segmentation that is capable of digging the physical imaging mechanism in atmosphere turbulence conditions, in order to improve semantic estimates. First, we investigate the physical imaging mechanism in kinds of turbulence conditions, including the isotropic turbulence and the anisotropic turbulence. Physical turbulence parameters are considered, such as the anisotropic factor, turbulence inner and outer scales, refractive-index structure constant, general spectral power law and imaging distance. Second, based on the physical imaging model in various turbulence conditions and image processing algorithms, we construct the turbulence-degraded image datasets, including the turbulence-degraded Pascal VOC 2012 and ADE20K. The new datasets cover a wide range of turbulence scenes. Third, in order to obtain more accurate boundary information, we propose the Boundary-aware DeepLabv3+ network that is trained on the constructed turbulence-degraded image datasets for semantic segmentation in turbulence media. The proposed model extends DeepLabv3+ by adding simple yet effective Edge Aware Loss and Border Auxiliary Supervision Module, which is helpful to acquire precise boundary segmentation effect while confining the target in this boundary region. Finally, without any preprocessing, the semantic segmentation accuracy reached a performance of 87.95% mIoU on the Turbulencedegraded Pascal VOC 2012 Dataset.

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Section: Appendix Appendix Amentioning

confidence: 90%

Section: A Imaging Mechanism In Atmospheric Turbulence Mediamentioning

confidence: 99%

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Accurate Semantic Segmentation in Turbulence Media

Cui

Zhang

2019

IEEE Access

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“…This method produces high accuracy simulations, but it is computationally intensive and very time consuming. The image-based turbulence imaging simulations run quickly by adopting image processing methods such as image resampling and filtering [5][6][7][8]. The image blur and distort produced by atmosphere turbulence can be obtained with the theoretical models of atmosphere turbulence effects.…”

Section: Introductionmentioning

confidence: 99%

“…It can reflect the image distortion and coincide with real imaging device[5,6] and takes the form: ( ) . This function describes the spatial blur relationship between different image regions.…”

mentioning

confidence: 99%

Infrared imaging simulation through anisotropic atmosphere turbulence

Tan¹

2016

Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

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Atmosphere turbulence often degrades image quality in long range infrared imaging system due to intensity fluctuations, distortion, and blur effects. In this work, long range infrared imaging through anisotropic turbulence is simulated, which combines the theoretical turbulence effects models of optical waves (the angle of arrival fluctuations of optical wave and the modulation transfer function) under anisotropic non-Kolmogorov turbulence and the imaging processing algorithms (image convolution and interpolation methods). This simulation technique allows the performance assessment of a long range infrared imaging system under various anisotropic atmosphere turbulence conditions to be done in a cost efficient manner. Simulation uses single image containing no turbulence effects as an input and produces image sequences degraded by specified turbulence. Imaging distance, optics diameter, wavelength, anisotropic factor, general spectral power law, and turbulence strength are included. The influence of anisotropic non-Kolmogorov turbulence on the long range infrared imaging system is analyzed.

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Energy conservation: a third constraint on the turbulent point spread function

Charnotskii

2013

Opt. Eng

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Simulation of a laser range-gated SWIR imaging system in weak turbulence conditions

Cited by 4 publications

References 11 publications

Accurate Semantic Segmentation in Turbulence Media

Accurate Semantic Segmentation in Turbulence Media

Infrared imaging simulation through anisotropic atmosphere turbulence

Energy conservation: a third constraint on the turbulent point spread function

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