卷积神经网络
计算机科学
光学层析成像
断层摄影术
人工智能
光学相干层析成像
漫反射光学成像
光学
迭代重建
物理
作者
Thanh Huy Nguyen,Vy Bui,George Nehmetallah
出处
期刊:Optical Engineering
[SPIE - International Society for Optical Engineering]
日期:2018-04-01
卷期号:57 (04): 1-1
被引量:9
标识
DOI:10.1117/1.oe.57.4.043111
摘要
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.
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