Sep 19 – 22, 2022
Paul Scherer Institute, Villigen, Switzerland
Europe/Zurich timezone

A SEDCNN Machine Learning Model for Textured SAXS/WAXD Image Denoising

Sep 20, 2022, 5:44 PM
WHGA/Auditorium and online (Paul Scherer Institute, Villigen, Switzerland)

WHGA/Auditorium and online

Paul Scherer Institute, Villigen, Switzerland

Paul Scherrer Institute Forschungsstrasse 111 CH-5232-Villigen-PSI
Poster NOBUGS 2022


Chun Li (Institute of High Energy Physics, Chinese Academy of Sciences)


With the advancements on instrumentations of next-generation synchrotron light sources, methodologies for small angle x-ray scattering (SAXS)/wide angle x-ray diffraction (WAXD) experiments have dramatically changed. Such experiments have evolved into dynamic and multi-scale in-situ characterizations, leaving prolonged exposure time as well as radiation-induced damage a serious concern. However, reduction on exposure time and dose may result in noisy images with much lower signal-to-noise ratio, thus requiring powerful denoising mechanisms for information retrieval. Here, we tackle the problem from an algorithmic perspective by proposing a small, yet effective encoder-decoder-structured machine learning model for experimental SAXS/WAXD image denoising, allowing more room for exposure time and dose adjustment. From preprocessing to architecture design and final performance evaluation, our network provides a bespoke denoising solution for SAXS/WAXD experimental images. Compared with classic image processing models like U-Net, REDCNN, and PMRID for natural images, our proposed model demonstrates superior performance on highly textured SAXS/WAXD images.

Email address of presenting author

Primary author

Mr Zhongzheng Zhou (Institute of High Energy Physics, Chinese Academy of Sciences)


Chun Li (Institute of High Energy Physics, Chinese Academy of Sciences)

Presentation materials