Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the computational process that recovers a more informative image. It is particularly challenging with thick and complex samples such as organoids. Recent works that rely on supervised training show that deep learning is a powerful method to unwrap the phase; however, supervised approaches require large and representative datasets which are difficult to obtain for complex biological samples. Inspired by the concept of deep image priors, we propose a deep-learning-based method that does not need any training set. Our framework relies on an untrained convolutional neural network to accurately unwrap the phase while ensuring the consistency of the measurements. We experimentally demonstrate that the proposed method faithfully recovers the phase of complex samples on both real and simulated data. Our work paves the way to reliable phase imaging of thick and complex samples with QPI.

中文翻译：

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通过深度图像先验进行定量相位成像的稳健相位展开

定量相位成像 (QPI) 是一种新兴的无标记技术，可生成包含形态学和动态信息的图像，而无需造影剂。不幸的是，大多数成像系统都包含相位。相位展开是恢复更多信息图像的计算过程。对于类器官等厚而复杂的样品，这尤其具有挑战性。最近依赖监督训练的工作表明，深度学习是解开相位的强大方法；然而，监督方法需要大型且具有代表性的数据集，而复杂的生物样本很难获得这些数据集。受深度图像先验概念的启发，我们提出了一种不需要任何训练集的基于深度学习的方法。我们的框架依赖于未经训练的卷积神经网络来准确地展开相位，同时确保测量的一致性。我们通过实验证明，所提出的方法在真实和模拟数据上都忠实地恢复了复杂样本的相位。我们的工作为使用 QPI 对厚而复杂的样品进行可靠的相位成像铺平了道路。