Restoration of Ghost Imaging in Atmospheric Turbulence Based on Deep Learning

被引：0

作者：

Jiang, Chenzhe ^{[1
]}

Xu, Banglian ^{[1
]}

Zhang, Leihong ^{[2
]}

Zhang, Dawei ^{[2
]}

机构：

[1] Univ Shanghai Sci & Technol, Coll Commun & Art Design, Shanghai 200093, Peoples R China

[2] Univ Shanghai Sci & Technol, Sch Opt Elect & Comp Engn, Shanghai 200093, Peoples R China

来源：

CURRENT OPTICS AND PHOTONICS | 2023年 / 7卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Atmospheric turbulence; Deep learning; Generative adversarial network; Ghost imaging; QUANTUM;

D O I：

10.3807/COPP.2023.7.6.655

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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.

引用

页码：655 / 664

页数：10

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