Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy, and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition, in many practical applications, it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here, we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation. The most significant advantage of the resulting physics-enhanced deep neural network (PhysenNet) is that it can be used without training beforehand, thus eliminating the need for tens of thousands of labeled data. We take single-beam phase imaging as an example for demonstration. We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object, and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model. This opens up a new paradigm of neural network design, in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

中文翻译：

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使用未经训练的神经网络进行相位成像。

迄今为止，为光学计算成像（CI）提出的大多数神经网络都采用监督训练策略，因此需要大量训练来优化其权重和偏差。在许多实际应用中，在许多小时的数据采集中，不考虑环境和系统稳定性的要求，不可能获得足够数量的地面真实图像进行训练。在这里，我们建议通过将代表图像形成过程的完整物理模型合并到常规的深度神经网络中来克服此限制。最终的物理增强型深度神经网络（PhysenNet）的最大优势在于，无需事先培训即可使用它，从而消除了成千上万个标记数据的需求。我们以单光束相位成像为例进行演示。我们的实验表明，只需要向PhysenNet提供一个相位对象的单个衍射图样，它就可以自动优化网络，并最终通过神经网络和物理模型之间的相互作用产生对象相位。这开辟了神经网络设计的新范式，其中可以将将物理模型合并到神经网络中的概念得以推广，以解决许多其他CI问题。