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• W3096768366 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." @default.
• W3096768366 created "2020-11-09" @default.
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• W3096768366 date "2020-11-24" @default.
• W3096768366 modified "2023-10-10" @default.
• W3096768366 title "Sparse-view imaging of a fiber internal structure in holographic diffraction tomography via a convolutional neural network" @default.
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• W3096768366 doi "https://doi.org/10.1364/ao.404276" @default.
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