NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering

Abstract

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.