用卷积网络高效去噪二维相关谱，助力低光强实验数据还原
A Fully Convolutional Approach to Denoising 2D Correlation Spectra
- 全卷积架构处理任意尺寸相关谱，保持动力学结构
- 在低信噪比下仍能恢复复杂动态特征，结构保真度高
- 适合光子受限的XPCS等实验，计算快且抗过拟合
我们提出一种全卷积去噪自编码器（FC-DAE），专门用于二维动态相关性的表示，适用于多种实验技术。以同步辐射光子相关光谱（XPCS）中的双时间强度关联函数（C₂）为例，该模型可接受任意尺寸输入，有效保留不同动力学状态下的相关结构。训练使用在NSLS-II束线采集的真实C₂数据，并通过数据增强提升数据多样性、降低过拟合风险。在低信噪比条件下，FC-DAE成功恢复了复杂的动力学特征，同时保持结构保真度。通过定量指标评估重建可靠性，验证了其在光子受限和低剂量测量条件下的鲁棒去噪性能与高计算效率。
原文摘要 · Abstract (English)
We present a fully convolutional denoising autoencoder (FC-DAE) tailored for two-dimensional representations of dynamic correlations that is applicable to many experimental techniques. Here, we demonstrate its performance on two-time intensity correlation functions ($C_2$) from X-ray photon correlation spectroscopy (XPCS). Unlike conventional denoising autoencoders that are typically restricted to fixed input sizes, the FC-DAE accepts inputs of arbitrary dimensions while preserving correlation structures across diverse dynamical regimes. The model is trained using experimentally derived $C_2$ data collected at NSLS-II beamlines, with data augmentation applied to expand the diversity of the dataset and reduce overfitting. The FC-DAE successfully recovers intricate dynamical features in low signal-to-noise conditions while maintaining structural fidelity. To assess reconstruction reliability, we employ quantitative metrics to evaluate structural fidelity and identify potential model-induced bias. Our results demonstrate that the FC-DAE provides robust denoising performance with high computational efficiency, enabling recovery of XPCS dynamics under photon-limited and low-dose measurement conditions.
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