Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Restoration of Ghost Imaging in Atmospheric Turbulence Based on Deep Learning

  • Chenzhe Jiang (College of Communication and Art Design, University of Shanghai for Science and Technology) ;
  • Banglian Xu (College of Communication and Art Design, University of Shanghai for Science and Technology) ;
  • Leihong Zhang (School of Optical-electrical and Computer Engineering, University of Shanghai for Science and Technology) ;
  • Dawei Zhang (School of Optical-electrical and Computer Engineering, University of Shanghai for Science and Technology)
  • Received : 2023.08.04
  • Accepted : 2023.10.08
  • Published : 2023.12.25
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Abstract

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.

Keywords

Acknowledgement

National Natural Science Foundation of China (No. 62275153, 62005165); Shanghai Industrial Collaborative Innovation Project (HCXBCY-2022-006); projects sponsored by the development fund for Shanghai talents (No: 2021005).

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