Phase imaging always deals with the problem of phase invisibility when capturing objects with existing light sensors. However, there is a demand for multiplane full intensity measurements and iterative propagation process or reliance on reference in most conventional approaches. In this paper, we present an end-to-end compressible phase imaging method based on deep neural networks, which can implement phase estimation using only binary measurements. A thin diffuser as a preprocessor is placed in front of the image sensor to implicitly encode the incoming wavefront information into the distortion and local variation of the generated speckles. Through the trained network, the phase profile of the object can be extracted from the discrete grains distributed in the low-bit-depth pattern. Our experiments demonstrate the faithful reconstruction with reasonable quality utilizing a single binary pattern and verify the high redundancy of the information in the intensity measurement for phase recovery. In addition to the advantages of efficiency and simplicity compared to now available imaging methods, our model provides significant compressibility for imaging data and can therefore facilitate the low-cost detection and efficient data transmission.

中文翻译：

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使用低位深模式的基于学习的相位成像

在使用现有光传感器捕捉物体时，相位成像总是处理相位不可见性问题。然而，在大多数传统方法中，需要多平面全强度测量和迭代传播过程或依赖参考。在本文中，我们提出了一种基于深度神经网络的端到端可压缩相位成像方法，该方法可以仅使用二进制测量来实现相位估计。作为预处理器的薄扩散器放置在图像传感器的前面，以隐式地将传入的波前信息编码为生成散斑的失真和局部变化。通过训练好的网络，可以从分布在低位深模式中的离散颗粒中提取物体的相位轮廓。我们的实验证明了使用单个二进制模式以合理的质量进行了忠实的重建，并验证了用于相位恢复的强度测量中信息的高度冗余。与现有成像方法相比，除了效率和简单性的优势外，我们的模型还为成像数据提供了显着的可压缩性，因此可以促进低成本检测和高效数据传输。