无需控制光源，突破光学衍射极限实现纳米级超分辨成像
Breaking the Diffraction Barrier for Passive Sources: Parameter-Decoupled Superresolution Assisted by Physics-Informed Machine Learning
- 通过物理信息机器学习解耦多参数干扰，实现被动源超分辨
- 实验图像分辨率达衍射极限14倍以上（约13.5纳米），保真度超82%
- 适用于无法控源的真实场景，如活细胞成像和天体观测
我们提出一种参数解耦的超分辨框架，可在不依赖光源先验知识或控制的前提下，估计被动双点源的亚波长间距。该理论规避了部分相干性、亮度不平衡、随机相对相位及光子统计等复杂参数的估计难题。结合物理信息机器学习模型（在普通台式机上训练），进一步处理背景噪声、光子损失及质心/方向错位等实际缺陷。集成方法在真实生成的实验图像上实现超过衍射极限14倍的分辨率（对应约13.5纳米），保真度高于82%，性能媲美针对可控光源的最先进技术。关键优势在于对光源参数变化和独立噪声的鲁棒性，使其在无法控制光源的实际场景中具有应用潜力，如天体成像、活细胞显微镜与量子计量学。本工作弥合了被动系统理论超分辨极限与实际应用之间的关键鸿沟。
原文摘要 · Abstract (English)
We present a parameter-decoupled superresolution framework for estimating sub-wavelength separations of passive two-point sources without requiring prior knowledge or control of the source. Our theoretical foundation circumvents the need to estimate multiple challenging parameters such as partial coherence, brightness imbalance, random relative phase, and photon statistics. A physics-informed machine learning (ML) model (trained with a standard desktop workstation), synergistically integrating this theory, further addresses practical imperfections including background noise, photon loss, and centroid/orientation misalignment. The integrated parameter-decoupling superresolution method achieves resolution 14 and more times below the diffraction limit (corresponding to ~ 13.5 nm in optical microscopy) on experimentally generated realistic images with >82% fidelity, performance rivaling state-of-the-art techniques for actively controllable sources. Critically, our method's robustness against source parameter variability and source-independent noises enables potential applications in realistic scenarios where source control is infeasible, such as astrophysical imaging, live-cell microscopy, and quantum metrology. This work bridges a critical gap between theoretical superresolution limits and practical implementations for passive systems.
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