Denoising an X-ray image by exploring the power of its physical symmetry

Next-generation light source facilities offer extreme spatial and temporal resolving power, enabling multiscale, ultra-fast and dynamic characterizations. However, a trade-off between acquisition efficiency and data quality needs to be made to fully unleash the resolving potential, for which purpose powerful denoising algorithms to improve the signal-to-noise ratio of the acquired X-ray images are desirable. Yet, existing models based on machine learning mostly require massive and diverse labeled training data. Here we introduce a self-supervised pre-training algorithm with blind denoising capability by exploring the intrinsic physical symmetry of X-ray patterns without requiring high signal-to-noise ratio reference data. The algorithm is more efficient and effective than algorithms without symmetry involved, including an supervised algorithm. It allows us to recover physical information from spatially and temporally resolved data acquired in X-ray diffraction/scattering and pair distribution function experiments, where pattern symmetry is often well preserved. This study facilitates photon-hungry experiments as well as in situ experiments with dynamic loading.