Restoration of Ghost Imaging in Atmospheric Turbulence Based on Deep Learning

Ghost imaging (GI) technology is developing rapidly, but there are inevitably some limitations such as the influence of atmospheric turbulence. In this paper, we study a ghost imaging system in atmospheric turbulence and use a gamma-gamma (GG) model to simulate the medium to strong range of turbulen...

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Bibliographic Details
Published in:Current optics and photonics 2023, 7(6), , pp.655-664
Main Authors: Jiang, Chenzhe, Xu, Banglian, Zhang, Leihong, Zhang, Dawei
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric turbulence
Deep learning
Generative adversarial networks
Ghosts
Image restoration
Machine learning
Signal to noise ratio
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Summary:Ghost imaging (GI) technology is developing rapidly, but there are inevitably some limitations such as the influence of atmospheric turbulence. In this paper, we study a ghost imaging system in atmospheric turbulence and use a gamma-gamma (GG) model to simulate the medium to strong range of turbulence distribution. With a compressed sensing (CS) algorithm and generative adversarial network (GAN), the image can be restored well. We analyze the performance of correlation imaging, the influence of atmospheric turbulence and the restoration algorithm's effects. The restored image's peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) increased to 21.9 dB and 0.67 dB, respectively. This proves that deep learning (DL) methods can restore a distorted image well, and it has specific significance for computational imaging in noisy and fuzzy environments.
ISSN:2508-7266
2508-7274
DOI:10.3807/COPP.2023.7.6.655