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We demonstrate a deep-learning(DL)-based computational microscopy for high-throughput phase imaging by taking multiplexed measurements and employing deep neural networks (DNNs) based reconstruction. In particular, we develop a Bayesian convolutional neural network (BNN) to quantify the uncertainties of the DL inference, providing a surrogate estimate of the true prediction errors. The framework is demonstrated on a high-speed computational phase microscopy technique. We show the BNN is able to not only predict high-resolution phase images and but also provide a pixel-wise credibility map that evaluates the imperfections in the datasets and training process。
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