论文标题
深相解码器：使用未经训练的深神经网络的自校准阶段显微镜
Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network
论文作者
论文摘要
深度神经网络已成为用于计算成像的有效工具，包括透明样品的定量相显微镜。从强度重建阶段，当前的方法依赖于培训示例的监督学习；因此，它们的性能对培训和成像设置的匹配敏感。在这里，我们通过使用未经训练的深神经网络进行测量形成，提出了一种新的相显微镜方法，将图像事先和成像物理封装。我们的方法不需要任何训练数据，并且通过将网络的权重适合捕获的图像来同时重建所寻求的阶段和学生平面畸变。为了实验证明，我们盲目地重建了从整个焦点图像（即对畸变的明确了解）的定量阶段。
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 imaging physics. Our approach does not require any training data and simultaneously reconstructs the sought 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 images blindly (i.e. no explicit knowledge of the aberrations).
