NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering
Yunong Sun, Fei Xia

TL;DR
NeOTF is a novel guidestar-free neural method that enables robust broadband dynamic imaging through scattering media by accurately retrieving the optical transfer function with minimal speckle images, even under low signal-to-noise conditions.
Contribution
NeOTF introduces a neural-representation-based approach for OTF retrieval that does not require guidestars, improving dynamic imaging in time-varying scattering media.
Findings
Successfully performs dynamic imaging with low SNR and broadband illumination.
Robustly retrieves the system's OTF without guidestars in experiments.
Validated performance in time-varying scattering media using simulations.
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…
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Taxonomy
TopicsRandom lasers and scattering media · Optical Polarization and Ellipsometry · Optical Coherence Tomography Applications
