Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level, leading to higher image-reconstruction quality compared with their natural counterparts. However, prevailing design methods of CAHMs are based on Huygens-Fresnel theory, meta-atom optimization, numerical simulation and experimental verification, which results in a consumption of computing resources. Here, we applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design. A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory, which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images. The training results show that the normalized mean pixel error is about 3% on dataset. As verification, the metasurface prototypes are fabricated, simulated and measured. The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field, which demonstrates the effectiveness of our design. Encouragingly, this work provides a monolithic field-to-pattern design method for CAHMs, which paves a new route for the direct reconstruction of metasurfaces.

To increase the storage capacity in holographic data storage (HDS), the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in HDS. In this study, we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase. By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks (CNNs) to demodulate amplitude and phase respectively. The experimental system is simple, stable, and robust, and it only needs a single diffraction image to realize the direct demodulation of both amplitude and phase. To our investigation, this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

传统光学成像系统主要依靠阵列探测器对目标的空间分布进行探测来达到成像的目的。而单像素成像不需要阵列探测器，在探测端只需要使用一个单点探测器来记录光场的信号，然后利用关联算法来重构目标物体的图像信息。由于单点探测器的技术较为成熟，且成本较为低廉，因此这种成像方式在近些年得到了研究人员的广泛关注，期望单像素成像技术能够应用在X射线、红外、太赫兹等波段。另外，单像素成像技术在生物荧光成像、多光谱成像、三维成像、光场复振幅成像等应用领域也得到了深入的研究。其中光场波前的相位探测在天文观测、医学诊断、光学测量等领域至关重要，研究人员针对这一问题提出了多种基于单像素成像技术进行复振幅成像的方法，这些研究有效地拓展了单像素成像技术的实际应用场景。文中主要介绍了单像素成像技术的历史发展及其基本工作原理，并着重介绍了单像素成像技术在复振幅成像应用中的工作。

The unevenly distributed Lorentz–Gaussian beams are difficult to reproduce in practice, because they require modulation in both amplitude and phase terms. Here, a new linearly polarized Lorentz–Gauss beam modulated by a helical axicon (LGB-HA) is calculated, and the two various experimental generation methods of this beam, Fourier transform method (FTM) and complex-amplitude modulation (CAM) method, are depicted. Compared with the FTM, the CAM method can modulate the phase and amplitude simultaneously by only one reflection-type phase-only liquid crystal spatial light modulator. Both of the methods are coincident with the numerical results. Yet CAM is simpler, efficient, and has a higher degree of conformance through data comparison. In addition, considering some barriers exist in shaping and reappearing the complicated Lorentz–Gauss beam with heterogeneous distribution, the evolution regularities of the beams with different parameters (axial parameter, topological charge, and phase factor) were also implemented.

This Letter describes an approach to encode complex-amplitude light waves with spatiotemporal double-phase holograms (DPHs) for overcoming the limit of the space-bandwidth product (SBP) delivered by existing methods. To construct DPHs, two spatially macro-pixel encoded phase components are employed in the SBP-preserved resampling of complex holograms. Four generated sub-DPHs are displayed sequentially in time for high-quality holographic image reconstruction without reducing the image size or discarding any image terms when the DPHs are interweaved. The reconstructed holographic images contain more details and less speckle noise, with their signal-to-noise ratio and structure similarity index being improved by 14.64% and 78.79%, respectively.