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We present a deep learning approach for the rapid resolution enhancement of optical diffraction tomography. Once our three-dimensional U-net-based convolutional neural network learns an image translation between raw tomograms and total-variation-regularized tomograms, the trained network can fill in the missing cone of a measured refractive index tomogram and improve its resolution within seconds. We demonstrate the feasibility and generalizability of our approach on various biological samples, including bacteria, WBC, and NIH3T3.
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DongHun Ryu, Dongmin Ryu, YoonSeok Baek, Hyeongjoo Cho, Geon Kim, Young Seo Kim, Yongki Lee, Yoosik Kim, Hyun-Seok Min, Yong Keun Park, "Solving the missing cone problem of diffraction tomography using deep learning," Proc. SPIE 11653, Quantitative Phase Imaging VII, 116530G (5 March 2021); https://doi.org/10.1117/12.2584961