Turbulence-induced 2D correlated image distortion

Schwartzman, Armin; Alterman, Marina; Zamir, Rotem; Schechner, Yoav Y.

doi:10.1109/iccphot.2017.7951490

Cited by 27 publications

(24 citation statements)

References 44 publications

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“…In contrast, here, we use a full anisoplanatic simulator 2 for validation, and a modified version of the Schwartzman's warping simulator for training. 27 Our BM-CNN method also gains improved performance by incorporating BM registration prior to CNN processing. We study the performance of our BM-CNN method on four different turbulence levels at two noise levels.…”

Section: Introductionmentioning

confidence: 94%

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Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

et al. 2021

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We present a deep learning approach for restoring images degraded by atmospheric optical turbulence. We consider the case of terrestrial imaging over long ranges with a wide fieldof-view. This produces an anisoplanatic imaging scenario where turbulence warping and blurring vary spatially across the image. The proposed turbulence mitigation (TM) method assumes that a sequence of short-exposure images is acquired. A block matching (BM) registration algorithm is applied to the observed frames for dewarping, and the resulting images are averaged. A convolutional neural network (CNN) is then employed to perform spatially adaptive restoration. We refer to the proposed TM algorithm as the block matching and CNN (BM-CNN) method. Training the CNN is accomplished using simulated data from a fast turbulence simulation tool capable of producing a large amount of degraded imagery from declared truth images rapidly. Testing is done using independent data simulated with a different well-validated numerical wave-propagation simulator. Our proposed BM-CNN TM method is evaluated in a number of experiments using quantitative metrics. The quantitative analysis is made possible by virtue of having truth imagery from the simulations. A number of restored images are provided for subjective evaluation. We demonstrate that the BM-CNN TM method outperforms the benchmark methods in the scenarios tested. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 94%

“…The fast warping simulator used here is based on that presented by Schwartzman et al 27 This simulator works by creating 2D arrays of white Gaussian random noise. The noise is filtered with specially designed LSI filters to transform the white noise into random fields with realistic spatial tilt correlation statistics.…”

Section: Fast Warping Simulationmentioning

confidence: 99%

Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

et al. 2021

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“…These 2D approaches are based on empirical data, from which they can extract suitable statistics. This allows the effects of turbulence to be reduced to a 2D representation which can then be applied to clean images 32,33 .…”

Section: Turbulence Simulationmentioning

confidence: 99%

Analysis of deep learning architectures for turbulence mitigation in long-range imagery

Vint

Caterina

Soraghan

et al. 2020

Artificial Intelligence and Machine Learning in Defense Applications II

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In long range imagery, the atmosphere along the line of sight can result in unwanted visual effects. Random variations in the refractive index of the air causes light to shift and distort. When captured by a camera, this randomly induced variation results in blurred and spatially distorted images. The removal of such effects is greatly desired. Many traditional methods are able to reduce the effects of turbulence within images, however they require complex optimisation procedures or have large computational complexity. The use of deep learning for image processing has now become commonplace, with neural networks being able to outperform traditional methods in many fields. This paper presents an evaluation of various deep learning architectures on the task of turbulence mitigation. The core disadvantage of deep learning is the dependence on a large quantity of relevant data. For the task of turbulence mitigation, real life data is difficult to obtain, as a clean undistorted image is not always obtainable. Turbulent images were therefore generated with the use of a turbulence simulator. This was able to accurately represent atmospheric conditions and apply the resulting spatial distortions onto clean images. This paper provides a comparison between current state of the art image reconstruction convolutional neural networks. Each network is trained on simulated turbulence data. They are then assessed on a series of test images. It is shown that the networks are unable to provide high quality output images. However, they are shown to be able to reduce the effects of spatial warping within the test images. This paper provides critical analysis into the effectiveness of the application of deep learning. It is shown that deep learning has potential in this field, and can be used to make further improvements in the future.

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“…Scattering media limits visibility in terrestrial images as well, when imaging in fog, haze, or turbulence [30]. Single image dehazing methods, e.g.…”

Section: Related Workmentioning

confidence: 99%

Color Restoration of Underwater Images

Menaker¹,

Treibitz

Avidan

2017

Procedings of the British Machine Vision Conference 2017

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Underwater images suffer from color distortion and low contrast, because light is attenuated as it propagates through water. The attenuation varies with wavelength and depends both on the properties of the water body in which the image was taken and the 3D structure of the scene, making it difficult to restore the colors. Existing single underwater image enhancement techniques either ignore the wavelength dependency of the attenuation, or assume a specific spectral profile. We propose a new method that takes into account multiple spectral profiles of different water types, and restores underwater scenes from a single image.We show that by estimating just two additional global parameters -the attenuation ratios of the blue-red and blue-green color channels -the problem of underwater image restoration can be reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since we do not know the water type ahead of time, we try different parameter sets out of an existing library of water types. Each set leads to a different restored image and the one that best satisfies the Gray-World assumption is chosen. The proposed single underwater image restoration method is fully automatic and is based on a more comprehensive physical image formation model than previously used.We collected a dataset of real images taken in different locations with varying water properties and placed color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of the proposed method.

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Turbulence-induced 2D correlated image distortion

Cited by 27 publications

References 44 publications

Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

Deep learning for anisoplanatic optical turbulence mitigation in long-range imaging

Analysis of deep learning architectures for turbulence mitigation in long-range imagery

Color Restoration of Underwater Images

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