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.

中文翻译：

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深度相位解码器：具有未经训练的深度神经网络的自校准相位显微镜

深度神经网络已成为计算成像的有效工具，包括透明样品的定量相显微镜检查。为了从强度中重建阶段，当前的方法依赖于监督学习和训练示例。因此，它们的性能对训练和成像设置的匹配很敏感。在这里，我们提出了一种新的相位显微镜方法，即使用未经训练的深度神经网络进行测量形成，封装先验图像和系统物理。我们的方法不需要任何训练数据，并且通过将网络的权重拟合到捕获的图像来同时重建相位和光瞳平面像差。为了进行实验证明，我们在不了解像差的情况下，通过直焦强度图像重建了定量相位。