Towards adaptable synchrotron image restoration pipeline  

作　　者：Chun Li Xiao-Xue Bi Yu-Jun Zhang Zhen Zhang Li-Wen Wang Jian Zhuang Dong-Liang Chen Yu-Hui Dong Yi Zhang 

机构地区：[1]Beijing Synchrotron Radiation Facility,Institute of High Energy Physics,Chinese Academy of Sciences,Beijing 100049,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]Spallation Neutron Source Science Center,Dongguan 523803,China [4]National Synchrotron Radiation Laboratory,University of Science and Technology of China,Hefei 230029,China

出　　处：《Nuclear Science and Techniques》2024年第10期4-16,共13页核技术（英文）

基　　金：supported by the Beijing Natural Science Foundation(No.1234042);the National Key Research and Development Program for Young Scientists(No.2023YFA1609900);the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB37000000);the National Natural Science Foundation of China(No.12305371)。

摘　　要：Synchrotron microscopic data commonly suffer from poor image quality with degraded resolution incurred by instrumentation defects or experimental conditions.Image restoration methods are often applied to recover the reduced resolution,providing improved image details that can greatly facilitate scientific discovery.Among these methods,deconvolution techniques are straightforward,yet either require known prior information or struggle to tackle large experimental data.Deep learning(DL)-based super-resolution(SR)methods handle large data well,however data scarcity and model generalizability are problematic.In addition,current image restoration methods are mostly offline and inefficient for many beamlines where high data volumes and data complexity issues are encountered.To overcome these limitations,an online image-restoration pipeline that adaptably selects suitable algorithms and models from a method repertoire is promising.In this study,using both deconvolution and pretrained DL-based SR models,we show that different restoration efficacies can be achieved on different types of synchrotron experimental data.We describe the necessity,feasibility,and significance of constructing such an image-restoration pipeline for future synchrotron experiments.

关 键 词：SYNCHROTRON DECONVOLUTION Deep learning SUPER-RESOLUTION PIPELINE 

分 类 号：TL544[核科学技术—核技术及应用]