基于数据驱动的发射端波前校正鬼成像系统&#x0002A;

褚博文; 唐家奎; 郎森; 晏芳

doi:10.7523/j.ucas.2026.038

中国科学院大学学报 >

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DOI: https://doi.org/10.7523/j.ucas.2026.038

基于数据驱动的发射端波前校正鬼成像系统^*

褚博文 ,
唐家奎 ,
郎森 ,
晏芳

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1.中国科学院大学资源与环境学院,北京101408;
2.北京燕山地球关键带国家野外科学观测研究站,北京101408

收稿日期: 2026-03-10

修回日期: 2026-06-18

网络出版日期: 2026-06-18

基金资助

^*京津冀环境综合治理国家科技重大专项（2025ZD1205000）资助

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A data-driven Ghost Imaging System with transmitter-end wavefront correction

Bowen Chu ,
Jiakui Tang ,
Sen Lang ,
Fang Yan

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1 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China;
2 Beijing Yanshan Earth Critical Zone National Research Station, Beijing 101408, China

Received date: 2026-03-10

Revised date: 2026-06-18

Online published: 2026-06-18

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摘要

鬼成像技术在生物医学、遥感探测与水下通信等领域极具应用潜力。该技术基于二阶相关成像的统计特性,对接收路径上的散射干扰具有良好的抗性。然而,若照明路径出现波前散射畸变,散斑的关联性会被严重破坏,导致成像质量骤降。现有解决方案多依赖手工设计投影图案或进行复杂的物理建模。这些方法泛化能力弱、效率低,难以应对多样化的静态畸变环境。为此,本研究提出了重度散射干扰的鬼成像波前预补偿方法,设计了一套基于发射端波前优化闭环反馈的鬼成像系统,以期改善重度散射干扰下的成像质量。该系统引入深度学习技术,设计了反馈调节散斑畸变网络（FRSD-Net）,通过闭环反馈机制智能生成最优照明散斑,有效保证了光场在穿透重度非均匀介质后,仍能保持高信噪比与强关联特性。实验表明,在水渍及聚合物等重度畸变场景下,该系统显著提升了鬼成像的质量。图像的PSNR/SSIM分别从6.45/0.12提升至22.00/0.73,以及从15.71/0.32提升至23.19/0.68。该方案有效结合了DMD的高速调制优势与深度学习的建模能力,为鬼成像在复杂散射环境中的实际应用提供了新路径。

关键词： 鬼成像; 波前整形; 数字微镜器件; 深度学习; 散射补偿

本文引用格式

褚博文 , 唐家奎 , 郎森 , 晏芳 . 基于数据驱动的发射端波前校正鬼成像系统^*[J]. 中国科学院大学学报, 0 : 38 . DOI: 10.7523/j.ucas.2026.038

Abstract

Ghost imaging (GI) technology holds significant application potential in fields such as biomedicine, remote sensing, and underwater communication. Based on the statistical characteristics of second-order correlation imaging, this technology exhibits strong robustness against scattering interference in the receiving path. However, when wavefront scattering distortion occurs in the illumination path, the speckle correlation is severely degraded, leading to a drastic decline in imaging quality. Existing solutions mostly rely on manually designed projection patterns or complex physical modeling. These methods suffer from weak generalization ability and low efficiency, making it difficult to cope with diverse static distortion environments. To address this, this study proposes a wavefront pre-compensation method for ghost imaging under severe scattering interference and designs a ghost imaging system based on closed-loop feedback of transmitter-end wavefront optimization. By introducing deep learning techniques, the system incorporates a Feedback-Regulated Speckle Distortion Network (FRSD-Net). Through a closed-loop feedback mechanism, it intelligently generates optimal illumination speckles, effectively ensuring that the light field maintains a high signal-to-noise ratio (SNR) and strong correlation characteristics even after penetrating severely inhomogeneous media. Experimental results demonstrate that the proposed system significantly improves ghost imaging quality under severe distortion scenarios, such as those caused by water stains and polymers. The PSNR/SSIM of the reconstructed images increased from 6.45/0.12 to 22.00/0.73, and from 15.71/0.32 to 23.19/0.68, respectively. This approach effectively combines the high-speed modulation advantages of Digital Micromirror Devices (DMD) with the modeling capabilities of deep learning, providing a new pathway for the practical application of ghost imaging in complex scattering environments.

Key words： Ghost imaging; Wavefront shaping; Digital micromirror device; Deep learning; Scattering compensation

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