用神经网络同时重建未知探针下的X射线相位成像，提升低剂量条件下的成像质量。
Learning neural representations for X-ray ptychography reconstruction with unknown probes
- 将物体和探针都建模为连续神经表示，端到端从衍射图直接重建图像。
- 在模拟与实验数据上均实现更优重建质量，尤其在低信号条件下表现稳健。
- 适用于多种显微成像逆问题，适合材料、生物等领域的高精度成像需求。
X射线叠印术可提供出色的纳米级分辨率，在材料科学、生物学和纳米技术中广泛应用。然而，当照明探针未知时，其成像重建面临严峻挑战，极大限制了技术潜力。传统迭代方法和深度学习方法在低剂量、高速实验的低信号条件下常表现不佳，影响重建保真度并阻碍技术普及。本文提出叠印隐式神经表征（PtyINR），一种自监督框架，可同时解决物体与探针恢复问题。通过将两者均参数化为连续神经表示，PtyINR直接从原始衍射图进行端到端重建，无需探针预标定。大量评估表明，PtyINR在模拟与实验数据上均实现卓越重建质量，且在低信号条件下具有显著鲁棒性。此外，PtyINR提供了一种通用、物理信息引导的框架，适用于多种依赖探针的逆问题，可推广至广泛的计算显微成像任务。
原文摘要 · Abstract (English)
X-ray ptychography provides exceptional nanoscale resolution and is widely applied in materials science, biology, and nanotechnology. However, its full potential is constrained by the critical challenge of accurately reconstructing images when the illuminating probe is unknown. Conventional iterative methods and deep learning approaches are often suboptimal, particularly under the low-signal conditions inherent to low-dose and high-speed experiments. These limitations compromise reconstruction fidelity and restrict the broader adoption of the technique. In this work, we introduce the Ptychographic Implicit Neural Representation (PtyINR), a self-supervised framework that simultaneously addresses the object and probe recovery problem. By parameterizing both as continuous neural representations, PtyINR performs end-to-end reconstruction directly from raw diffraction patterns without requiring any pre-characterization of the probe. Extensive evaluations demonstrate that PtyINR achieves superior reconstruction quality on both simulated and experimental data, with remarkable robustness under challenging low-signal conditions. Furthermore, PtyINR offers a generalizable, physics-informed framework for addressing probe-dependent inverse problems, making it applicable to a wide range of computational microscopy problems.
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