Computational ghost imaging using deep learning
Computational ghost imaging using deep learning
复制标题
DOI：
10.1016/j.optcom.2017.12.041
复制
发表时间：
2018-04-15
影响因子：
2.4
通讯作者：
Ito, Tomoyoshi
中科院分区：
文献类型：
--
作者：
Shimobaba, Tomoyoshi;Endo, Yutaka;Ito, Tomoyoshi
计算重影成像 (CGI) 是一种单像素成像技术，它利用已知随机图案与测量到的物体透射（或反射）光强度之间的相关性。虽然CGI可以用单个或几个桶检测器获得二维或三维图像，但由于从随机模式重建图像，重建图像的质量会因噪声而降低。在这项研究中，我们利用深度学习提高了 CGI 图像的质量。深度神经网络用于自动学习受噪声污染的 CGI 图像的特征。训练后，网络能够从新的受噪声污染的 CGI 图像中预测低噪声图像。 (C) 2017 Elsevier B.V. 保留所有权利。
Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two-or three-dimensional images with a single or a few bucket detectors, the quality of the reconstructed images is reduced by noise due to the reconstruction of images from random patterns. In this study, we improve the quality of CGI images using deep learning. A deep neural network is used to automatically learn the features of noise-contaminated CGI images. After training, the network is able to predict low-noise images from new noise-contaminated CGI images. (C) 2017 Elsevier B.V. All rights reserved.