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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.22328 |
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| _version_ | 1866915631697231872 |
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| author | Sun, Yunong Xia, Fei |
| author_facet | Sun, Yunong Xia, Fei |
| contents | Dynamic imaging through time-varying scattering media is ubiquitous in real-world settings, yet it remains a defining unsolved problem as rapid spatiotemporal fluctuations overwhelm standard reconstruction pipelines that often rely on speckles with high signal-to-noise ratio. Existing approaches fall into two categories. Guidestar-based methods employ a guidestar to recover the system transfer function; however, in dynamic media, the speckle decorrelates rapidly, making the calibration quickly invalid. Guidestar-free methods infer information from speckle statistics, but rapid changes and noise often break phase retrieval. To overcome these limitations, we introduce NeOTF, a guidestar-free and neural-representation-based OTF retrieval method that enables dynamic imaging through time-varying scattering media. By optimizing this neural representation with only a few speckle images from unknown objects, NeOTF robustly retrieves the system's OTF without a guidestar. We experimentally demonstrate robust dynamic imaging through scattering with NeOTF at extremely low signal-to-noise ratio and broadband incoherent illumination (up to 300 nm spectral bandwidth) scenarios, and we numerically validate its dynamic imaging performance in time-varying scattering media leveraging spatio-temporal memory effect. Finally, we discuss and analyze its computational efficiency and generalization capabilities across anisotropic scattering media. These results establish NeOTF's promise as a practical and robust solution for dynamic imaging through scattering media. Open-sourced code and models are available at https://github.com/Xia-Research-Lab/NeOTF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering Sun, Yunong Xia, Fei Optics Applied Physics Dynamic imaging through time-varying scattering media is ubiquitous in real-world settings, yet it remains a defining unsolved problem as rapid spatiotemporal fluctuations overwhelm standard reconstruction pipelines that often rely on speckles with high signal-to-noise ratio. Existing approaches fall into two categories. Guidestar-based methods employ a guidestar to recover the system transfer function; however, in dynamic media, the speckle decorrelates rapidly, making the calibration quickly invalid. Guidestar-free methods infer information from speckle statistics, but rapid changes and noise often break phase retrieval. To overcome these limitations, we introduce NeOTF, a guidestar-free and neural-representation-based OTF retrieval method that enables dynamic imaging through time-varying scattering media. By optimizing this neural representation with only a few speckle images from unknown objects, NeOTF robustly retrieves the system's OTF without a guidestar. We experimentally demonstrate robust dynamic imaging through scattering with NeOTF at extremely low signal-to-noise ratio and broadband incoherent illumination (up to 300 nm spectral bandwidth) scenarios, and we numerically validate its dynamic imaging performance in time-varying scattering media leveraging spatio-temporal memory effect. Finally, we discuss and analyze its computational efficiency and generalization capabilities across anisotropic scattering media. These results establish NeOTF's promise as a practical and robust solution for dynamic imaging through scattering media. Open-sourced code and models are available at https://github.com/Xia-Research-Lab/NeOTF. |
| title | NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering |
| topic | Optics Applied Physics |
| url | https://arxiv.org/abs/2507.22328 |