Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909931293114368 |
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| author | Li, Yue-Gang Zheng, Ze Wang, Jun-jie He, Ming Fan, Jianping Xiao, Tailong Zeng, Guihua |
| author_facet | Li, Yue-Gang Zheng, Ze Wang, Jun-jie He, Ming Fan, Jianping Xiao, Tailong Zeng, Guihua |
| contents | Ghost imaging leverages a single-pixel detector with no spatial resolution to acquire object echo intensity signals, which are correlated with illumination patterns to reconstruct an image. This architecture inherently mitigates scattering interference between the object and the detector but sensitive to scattering between the light source and the object. To address this challenge, we propose an optical diffraction neural networks (ODNNs) assisted ghost imaging method for imaging through dynamic scattering media. In our scheme, a set of fixed ODNNs, trained on simulated datasets, is incorporated into the experimental optical path to actively correct random distortions induced by dynamic scattering media. Experimental validation using rotating single-layer and double-layer ground glass confirms the feasibility and effectiveness of our approach. Furthermore, our scheme can also be combined with physics-prior-based reconstruction algorithms, enabling high-quality imaging under undersampled conditions. This work demonstrates a novel strategy for imaging through dynamic scattering media, which can be extended to other imaging systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media Li, Yue-Gang Zheng, Ze Wang, Jun-jie He, Ming Fan, Jianping Xiao, Tailong Zeng, Guihua Optics Machine Learning Ghost imaging leverages a single-pixel detector with no spatial resolution to acquire object echo intensity signals, which are correlated with illumination patterns to reconstruct an image. This architecture inherently mitigates scattering interference between the object and the detector but sensitive to scattering between the light source and the object. To address this challenge, we propose an optical diffraction neural networks (ODNNs) assisted ghost imaging method for imaging through dynamic scattering media. In our scheme, a set of fixed ODNNs, trained on simulated datasets, is incorporated into the experimental optical path to actively correct random distortions induced by dynamic scattering media. Experimental validation using rotating single-layer and double-layer ground glass confirms the feasibility and effectiveness of our approach. Furthermore, our scheme can also be combined with physics-prior-based reconstruction algorithms, enabling high-quality imaging under undersampled conditions. This work demonstrates a novel strategy for imaging through dynamic scattering media, which can be extended to other imaging systems. |
| title | Optical diffraction neural networks assisted computational ghost imaging through dynamic scattering media |
| topic | Optics Machine Learning |
| url | https://arxiv.org/abs/2511.22913 |