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Deep-learning projector for optical diffraction tomography
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DOI：10.1364/OE.381413.png)
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Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been derived by the minimization of an objective function in which the data-fidelity term encourages measurement consistency while the regularization term enforces prior constraints. In this work, we propose to train a convolutional neural network (CNN) as the projector in a projected-gradient-descent method. We iteratively produce high-quality estimates and ensure measurement consistency, thus keeping the best of CNN-based and regularization-based worlds. Our experiments on two-dimensional-simulated and real data show an improvement over other conventional or deep-learning-based methods. Furthermore, our trained CNN projector is general enough to accommodate various forward models for the handling of multiple-scattering events. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
REFRACTIVE-INDEX
IMAGE-RECONSTRUCTION
MULTIPLE-SCATTERING
INVERSE PROBLEMS
NEURAL-NETWORK
ALGORITHMS
EFFICIENT
CELLS
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
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引用论文
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