无需干净参考数据，用自监督学习实现低光鬼成像的高精度重建。
Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging
- 引入自监督深度卷积网络，无需真实参考图像即可重建。
- 在噪声数据下实现现有无监督方法中最优的成像质量。
- 适用于低剂量X射线荧光成像等极限低光场景，如生物与电池研究。
我们提出一种新的基于自监督深度学习的鬼成像（GI）重建方法，在无监督方法中实现了前所未有的噪声环境下成像质量。本文提供了支持性的数学框架以及理论和真实数据的实验结果。自监督机制消除了对干净参考数据的需求，同时具备强大的降噪能力，为新兴的前沿低光鬼成像应用中信号-噪声比问题提供了必要工具。典型应用场景包括微纳米尺度的X射线发射成像，例如对辐射敏感样品的X射线荧光成像，其应用涵盖生物样本的原位及工作状态下的案例研究，以及电池系统的在役分析。
原文摘要 · Abstract (English)
We present a new self-supervised deep-learning-based Ghost Imaging (GI) reconstruction method, which provides unparalleled reconstruction quality for noisy acquisitions among unsupervised methods. We present the supporting mathematical framework and results from theoretical and real data use cases. Self-supervision removes the need for clean reference data while offering strong noise reduction. This provides the necessary tools for addressing signal-to-noise ratio concerns for GI acquisitions in emerging and cutting-edge low-light GI scenarios. Notable examples include micro- and nano-scale x-ray emission imaging, e.g., x-ray fluorescence imaging of dose-sensitive samples. Their applications include in-vivo and in-operando case studies for biological samples and batteries.
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