Li Jingxi, Li Yuhang, Gan Tianyi, Shen Che-Yung, Jarrahi Mona, Ozcan Aydogan
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Light Sci Appl. 2024 May 28;13(1):120. doi: 10.1038/s41377-024-01482-6.
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.
复场成像能够捕获输入光场或物体的幅度和相位信息，可提供有关样本的丰富结构信息，比如其吸收和折射率分布。然而，传统图像传感器基于强度，本质上缺乏直接测量场相位分布的能力。使用干涉或全息方法可以克服这一限制，通常还需辅以迭代相位检索算法，这会导致硬件复杂度和计算需求大幅增加。在此，我们提出一种复场成像仪设计，该设计能使用基于强度的传感器阵列对输入场的幅度和定量相位信息进行快照成像，而无需任何数字处理。我们的设计利用经过深度学习优化的连续衍射面，这些衍射面的结构可共同调制输入复场，形成两个独立的成像通道，在紧凑的光学设计中，在输入和输出平面之间执行幅度到幅度以及相位到强度的转换，轴向跨度约为100个波长。传感器平面上这两个通道处输出场的强度分布直接对应于输入复场的幅度和定量相位分布，无需任何数字图像重建算法。我们通过在太赫兹光谱下工作的3D打印原型对复场衍射成像仪设计的有效性进行了实验验证，输出的幅度和相位通道图像与我们的数值模拟结果紧密吻合。我们设想这种复场成像仪将在安全、生物医学成像、传感和材料科学等领域有多种应用。
