期刊
BIOMEDICAL OPTICS EXPRESS
卷 11, 期 2, 页码 1107-1121出版社
Optica Publishing Group
DOI: 10.1364/BOE.379533
关键词
-
资金
- European Research Council [678316]
- Clore Israel Foundation
- European Research Council (ERC) [678316] Funding Source: European Research Council (ERC)
We present a deep-learning approach for solving the problem of 2 pi phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoder-decoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据