A machine learning model for textured X-ray scattering and diffraction image denoising

作者：Zhongzheng Zhou, Chun Li, Xiaoxue Bi, Chenglong Zhang, Yingke Huang, Jian Zhuang, Wenqiang Hua, Zheng Dong, Lina Zhao, Yi Zhang, Yuhui Dong · 发表于：npj Computational Materials · 年份：2023 · DOI：10.1038/s41524-023-01011-w · 被引用次数：24 · 研究领域：Medical Imaging Techniques and Applications、Advanced X-ray Imaging Techniques、Advanced MRI Techniques and Applications

Abstract With the advancements in instrumentations of next-generation synchrotron light sources, methodologies for small-angle X-ray scattering (SAXS)/wide-angle X-ray diffraction (WAXD) experiments have dramatically evolved. Such experiments have developed into dynamic and multiscale in situ characterizations, leaving prolonged exposure time as well as radiation-induced damage a serious concern. However, reduction on exposure time or dose may result in noisier images with a lower signal-to-noise ratio, requiring powerful denoising mechanisms for physical information retrieval. Here, we tackle the problem from an algorithmic perspective by proposing a small yet effective machine-learning model for experimental SAXS/WAXD image denoising, allowing more redundancy for exposure time or dose reduction. Compared with classic models developed for natural image scenarios, our model provides a bespoke denoising solution, demonstrating superior performance on highly textured SAXS/WAXD images. The model is versatile and can be applied to denoising in other synchrotron imaging experiments when data volume and image complexity is concerned.