Yolalmaz Alim, Yüce Emre
Programmable Photonics Group, Department of Physics, Middle East Technical University, 06800, Ankara, Turkey.
Micro and Nanotechnology Program, Middle East Technical University, 06800, Ankara, Turkey.
Sci Rep. 2022 Feb 15;12(1):2487. doi: 10.1038/s41598-022-06190-y.
Holography is a vital tool used in various applications from microscopy, solar energy, imaging, display to information encryption. Generation of a holographic image and reconstruction of object/hologram information from a holographic image using the current algorithms are time-consuming processes. Versatile, fast in the meantime, accurate methodologies are required to compute holograms performing color imaging at multiple observation planes and reconstruct object/sample information from a holographic image for widely accommodating optical holograms. Here, we focus on design of optical holograms for generation of holographic images at multiple observation planes and colors via a deep learning model, the CHoloNet. The CHoloNet produces optical holograms which show multitasking performance as multiplexing color holographic image planes by tuning holographic structures. Furthermore, our deep learning model retrieves an object/hologram information from an intensity holographic image without requiring phase and amplitude information from the intensity image. We show that reconstructed objects/holograms show excellent agreement with the ground-truth images. The CHoloNet does not need iteratively reconstruction of object/hologram information while conventional object/hologram recovery methods rely on multiple holographic images at various observation planes along with the iterative algorithms. We openly share the fast and efficient framework that we develop in order to contribute to the design and implementation of optical holograms, and we believe that the CHoloNet based object/hologram reconstruction and generation of holographic images will speed up wide-area implementation of optical holography in microscopy, data encryption, and communication technologies.
全息术是一种重要工具，应用于从显微镜学、太阳能、成像、显示到信息加密等各种领域。使用当前算法生成全息图像以及从全息图像重建物体/全息图信息是耗时的过程。需要通用、快速且准确的方法来计算全息图，以在多个观察平面上执行彩色成像，并从全息图像重建物体/样本信息，从而广泛适用于光学全息图。在此，我们专注于通过深度学习模型CHoloNet设计用于在多个观察平面和多种颜色下生成全息图像的光学全息图。CHoloNet通过调整全息结构生成具有多任务性能的光学全息图，可复用彩色全息图像平面。此外，我们的深度学习模型无需强度图像的相位和幅度信息就能从强度全息图像中检索物体/全息图信息。我们展示了重建的物体/全息图与真实图像高度吻合。CHoloNet无需迭代重建物体/全息图信息，而传统的物体/全息图恢复方法则依赖于在不同观察平面上的多个全息图像以及迭代算法。我们公开分享我们开发的快速高效框架，以助力光学全息图的设计与实现，并且我们相信基于CHoloNet的物体/全息图重建以及全息图像生成将加速光学全息术在显微镜学、数据加密和通信技术中的广泛应用。
