Deep-learning-based image registration for nano-resolution tomographic reconstruction

Fu, T.; Zhang, K.; Wang, Y.; Li, J.; Zhang, J.; Yao, C.; He, Q.; Wang, S.; Huang, W.; Yuan, Q.; Pianetta, P.; Liu, Y.

doi:10.1107/S1600577521008481

Journal of Synchrotron Radiation Journal of
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Figure 1
The workflow of the deep-learning-based image jitter correction method for synchrotron nano-resolution tomographic. Step A is the reconstruction of the raw projective images. Step B is reprojection of the sample from different perspectives. Step C acquires image jitter by the residual neural network. Step D corrects the projective images. Step E is to reconstruct the 3D image after correction.

JOURNAL OF
SYNCHROTRON
RADIATION

ISSN: 1600-5775

Volume 28| Part 6| November 2021| Pages 1909-1915

https://doi.org/10.1107/S1600577521008481

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