Deep-learning-based image registration for nano-resolution tomographic reconstruction
Tianyu Fu, Kai Zhang, Yan Wang, Jizhou Li, Jin Zhang, Chunxia Yao, Qili He, Shanfeng Wang, Wanxia Huang, Qingxi Yuan, P. Pianetta, Yijin Liu
Abstract
Nano-resolution full-field transmission X-ray microscopy has been successfully applied to a wide range of research fields thanks to its capability of non-destructively reconstructing the 3D structure with high resolution. Due to constraints in the practical implementations, the nano-tomography data is often associated with a random image jitter, resulting from imperfections in the hardware setup. Without a proper image registration process prior to the reconstruction, the quality of the result will be compromised. Here a deep-learning-based image jitter correction method is presented, which registers the projective images with high efficiency and accuracy, facilitating a high-quality tomographic reconstruction. This development is demonstrated and validated using synthetic and experimental datasets. The method is effective and readily applicable to a broad range of applications. Together with this paper, the source code is published and adoptions and improvements from our colleagues in this field are welcomed.