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

Bowen Chu; Jiakui Tang; Sen Lang; Fang Yan

doi:10.7523/j.ucas.2026.038

Journal of University of Chinese Academy of Sciences >

0 38

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

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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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

Cite this article

Bowen Chu , Jiakui Tang , Sen Lang , Fang Yan . A data-driven Ghost Imaging System with transmitter-end wavefront correction[J]. Journal of University of Chinese Academy of Sciences, 0 : 38 . DOI: 10.7523/j.ucas.2026.038

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