A Fully Convolutional Approach to Denoising 2D Correlation Spectra

Abstract: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.

Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2605.29975 [cs.LG]

(or arXiv:2605.29975v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.29975 Focus to learn more arXiv-issued DOI via DataCite

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