Sparse-view imaging of a fiber internal structure in holographic diffraction tomography via a convolutional neural network.
Appl Opt
; 60(4): A234-A242, 2021 Feb 01.
Article
en En
| MEDLINE
| ID: mdl-33690374
ABSTRACT
Deep learning has recently shown great potential in computational imaging. Here, we propose a deep-learning-based reconstruction method to realize the sparse-view imaging of a fiber internal structure in holographic diffraction tomography. By taking the sparse-view sinogram as the input and the cross-section image obtained by the dense-view sinogram as the ground truth, the neural network can reconstruct the cross-section image from the sparse-view sinogram. It performs better than the corresponding filtered back-projection algorithm with a sparse-view sinogram, both in the case of simulated data and real experimental data.
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