用物理规律引导风格迁移，仅凭强度数据实现全息重建
Physics-Aware Style Transfer for Adaptive Holographic Reconstruction
- 将物距视为衍射图样的隐式风格，通过循环翻译学习逆映射
- 仅需强度测量数据即可完成自适应全息重构，无需复杂幅值真值
- 适用于动态红细胞等生物成像，适合实时无标记成像场景
原位全息成像面临从记录的衍射图样中重构物体复振幅的不适定逆问题。尽管深度学习方法在性能上优于传统相位恢复算法，但通常需要高质量的复振幅图真值数据集以实现两个域间的统计逆映射。本文提出一种物理感知的风格迁移方法，将物体到传感器的距离视为衍射图样中的隐式风格。利用该风格域作为中间域构建循环图像转换，证明仅使用强度测量数据集即可实现自适应的逆映射学习。进一步通过动态流动红细胞形态重构展示了其生物医学应用潜力，凸显了其在实时、无标记成像中的前景。作为一种利用测量中固有物理线索的框架，该方法为真值难以获取的成像应用提供了实用的学习策略。
原文摘要 · Abstract (English)
Inline holographic imaging presents an ill-posed inverse problem of reconstructing objects' complex amplitude from recorded diffraction patterns. Although recent deep learning approaches have shown promise over classical phase retrieval algorithms, they often require high-quality ground truth datasets of complex amplitude maps to achieve a statistical inverse mapping operation between the two domains. Here, we present a physics-aware style transfer approach that interprets the object-to-sensor distance as an implicit style within diffraction patterns. Using the style domain as the intermediate domain to construct cyclic image translation, we show that the inverse mapping operation can be learned in an adaptive manner only with datasets composed of intensity measurements. We further demonstrate its biomedical applicability by reconstructing the morphology of dynamically flowing red blood cells, highlighting its potential for real-time, label-free imaging. As a framework that leverages physical cues inherently embedded in measurements, the presented method offers a practical learning strategy for imaging applications where ground truth is difficult or impossible to obtain.
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