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26 April 2018 Computational optical tomography using 3-D deep convolutional neural networks
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Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2018/$25.00 © 2018 SPIE
Thanh C. Nguyen, Vy Bui, and George Nehmetallah "Computational optical tomography using 3-D deep convolutional neural networks," Optical Engineering 57(4), 043111 (26 April 2018).
Received: 4 January 2018; Accepted: 3 April 2018; Published: 26 April 2018

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