摘要
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.
基金
This work was funded by the National Science Foundation for Young Scientists of China(Grant No.12005253)
the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB 37000000)
the Innovation Program of the Institute of High Energy Physics,CAS(No.E25455U210).All authors gratefully acknowledge the support from the BL10U1 and BL19U2 beamline at Shanghai Synchrotron Radiation Facility(SSRF),the I22 beamline at Diamond Light Source,and the 1W2A and 3W1A beamline at Beijing Synchrotron Radiation Facility(BSRF)for generously offering beamtime to acquire experimental data.
