Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network
Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network
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DOI：
10.1364/optica.389314
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发表时间：
2020-06-20
期刊：
影响因子：
10.4
通讯作者：
Waller, Laura
中科院分区：
文献类型：
--
作者：
Bostan, Emrah;Heckel, Reinhard;Waller, Laura
深度神经网络已经成为计算成像的有效工具，包括透明样品的定量相位显微镜。为了从强度重建相位，当前的方法依赖于具有训练示例的监督学习;因此，它们的性能对训练和成像设置的匹配敏感。在这里，我们提出了一种新的相位显微镜方法，通过使用未经训练的深度神经网络进行测量形成，封装图像先验和系统物理。我们的方法不需要任何训练数据，同时重建相位和瞳孔平面像差通过拟合网络的权重捕获的图像。为了证明实验，我们重建定量相位从离焦强度图像没有知识的像差。(C)根据OSA开放获取出版协议的条款，2020年美国光学学会。
Deep neural networks have emerged as effective tools for computational imaging, including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity, current approaches rely on supervised learning with training examples; consequently, their performance is sensitive to a match of training and imaging settings. Here we propose a new approach to phase microscopy by using an untrained deep neural network for measurement formation, encapsulating the image prior and the system physics. Our approach does not require any training data and simultaneously reconstructs the phase and pupil-plane aberrations by fitting the weights of the network to the captured images. To demonstrate experimentally, we reconstruct quantitative phase from through-focus intensity images without knowledge of the aberrations. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.