Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

翻译：复场成像能够同时捕获输入光场或物体的振幅与相位信息，从而揭示样品结构特征（如吸收率和折射率分布）的丰富细节。然而，传统图像传感器仅能感知光强，本质上无法直接测量光场的相位分布。这一局限性可通过干涉或全息方法（常辅以迭代相位恢复算法）来克服，但这会导致硬件复杂度与计算需求显著增加。本文提出一种无需数字处理的复场成像器设计，可直接利用强度型传感器阵列对输入光场的振幅与定量相位信息进行快照式成像。该设计采用多层深度学习优化的衍射表面，通过结构化排布对输入复场进行联合调制，在紧凑光学结构中形成两个独立成像通道，轴向跨度约100个波长，分别实现从输入平面到输出平面的振幅-振幅变换与相位-强度变换。传感器平面上两个通道的输出光场强度分布直接对应输入复场的振幅与定量相位分布，完全无需任何数字图像重建算法。我们通过太赫兹波段的3D打印原型器件实验验证了复场衍射成像器设计的效果，其输出振幅与相位通道图像与数值模拟结果高度吻合。我们预期该复场成像器将在安防、生物医学成像、传感及材料科学等领域具有广泛应用前景。