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

Abstract

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.