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The research and development of methods using of the specialized diffractive microstructure sensors embedded in the pattern of computer-generated holograms (CGH) manufactured on circular and X-Y laser writing systems is discussed. These microstructures consist of two parts: one of which is written before the CGH in the field of future hologram and the second one is written during the long-term writing of the CGH. The shift between the first and second part of the microstructure is the trace of the writing errors and allows one to determine and calculate the error of CGH fabrication along both orthogonal coordinates. The developed method is based on the principle of diffraction-based overlay with 1D and 2D built-in diffractive microstructure-sensors. Mathematical modeling and results of experimental test writings of such diffractive microstructure sensors are described. The efficiency of using these types of build-in sensors for the writing errors estimation for CGHs is demonstrated.

Herein, we have discussed three major methods which have been generally employed for the generation of optical beams with orbital angular momentum (OAM). These methods include the practice of diffractive optics elements (DOEs), metasurfaces (MSs), and photonic integrated circuits (PICs) for the production of in-plane and out-of-plane OAM. This topic has been significantly evolved as a result; these three methods have been further implemented efficiently by different novel approaches which are discussed as well. Furthermore, development in the OAM detection techniques has also been presented. We have tried our best to bring novel and up-to-date information to the readers on this interesting and widely investigated topic.

Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi‐depth phase‐only holograms using a fully convolutional neural network (FCN). The method primarily involves a forward–backward‐diffraction framework to compute multi‐depth diffraction fields, along with a layer‐by‐layer replacement method (L2RM) to handle occlusion relationships. The diffraction fields computed by the former are fed into the carefully designed FCN, which leverages its powerful non‐linear fitting capability to generate multi‐depth holograms of 3D scenes. The latter can smooth the boundaries of different layers in scene reconstruction by complementing information of occluded objects, thus enhancing the reconstruction quality of holograms. The proposed method can generate a multi‐depth 3D hologram with a PSNR of 31.8 dB in just 90 ms for a resolution of 2160 × 3840 on the NVIDIA Tesla A100 40G tensor core GPU. Additionally, numerical and experimental results indicate that the generated holograms accurately reconstruct clear 3D scenes with correct occlusion relationships and provide excellent depth focusing. This work introduces the forward–backward‐diffraction framework for computing multi‐depth diffraction fields and the layer‐by‐layer replacement method for handling occlusion relationships. When combined with a fully convolutional neural network, it generates multi‐depth holograms with excellent depth focusing and corrects occlusion relationships. The reconstructed scene exhibits minimal speckle noise and few edge artifacts.

We propose a compressive self-interference incoherent digital holography (SIDH) with a geometric phase metalens for section-wise holographic object reconstruction. We specify the details of the SIDH with a geometric phase metalens design that covers the visible wavelength band, analyze a spatial distortion problem in the SIDH and address a process of a compressive holographic section-wise reconstruction with analytic spatial calibration. The metalens allows us to realize a compressive SIDH system in the visible wavelength band using an image sensor with relatively low bandwidth. The operation of the proposed compressive SIDH is verified through numerical simulations.

Generating computer‐generated holograms (CGHs) for 3D scenes by learning‐based methods can reconstruct arbitrary 3D scenes with higher quality and faster speed. However, the homogenization and difficulty of obtaining 3D high‐resolution datasets seriously limit the generalization ability of the model. A novel approach is proposed to train 3D encoding models based on convolutional neural networks (CNNs) using 2D image datasets. This technique produces virtual depth (VD) images with a statistically uniform distribution. This approach employs a CNN trained with the angular spectrum method (ASM) for calculating diffraction fields layer by layer. A fully convolutional neural network architecture for phase‐only encoding, which is trained on the DIV2K‐VD dataset. Experimental results validate its effectiveness by generating a 4K phase‐only hologram within only 0.061 s, yielding high‐quality holograms that have an average PSNR of 34.7 dB along with an SSIM of 0.836, offering high quality, economic and time efficiencies compared to traditional methods. This study presents a novel method for generating computer‐generated holograms (CGHs) of 3D scenes using a CNN trained on 2D image datasets. By creating virtual depth (VD) images with a uniform statistical distribution, this approach enables fast reconstructions, achieving phase‐only holograms with an average PSNR of 34.7 dB and SSIM of 0.836, surpassing traditional techniques.

The main problem faced by traditional three-dimensional (3D) holographic displays is the time-consuming and poor flexibility of the hologram generation process. To address this issue, this paper proposes a non-iterative 3D computer-generated hologram (SFS-ORAP-PC-3D) method based on single full-support optimized random phase and phase compensation. Combining the full-support optimized random phase (FS-ORAP) method and the 3D layer-based idea to efficiently and non-iteratively generate the phase-only hologram of a 3D object with arbitrary positions and sizes using single FS-ORAP, thus overcoming the limitations of the original ORAP method in target position and size. Meanwhile, using a Fresnel lens for phase compensation allows for free selection of reconstruction planes. Numerical and optical experiments validate the feasibility of our proposed method.

Augmented reality (AR) technology has been widely applied across a variety of fields, with head-up displays (HUDs) being one of its prominent uses, offering immersive three-dimensional (3D) experiences and interaction with digital content and the real world. AR-HUDs face challenges such as limited field of view (FOV), small eye-box, bulky form factor, and absence of accommodation cue, often compromising trade-offs between these factors. Recently, optical waveguide based on pupil replication process has attracted increasing attention as an optical element for its compact form factor and exit-pupil expansion. Despite these advantages, current waveguide displays struggle to integrate visual information with real scenes because they do not produce accommodation-capable virtual content. In this paper, we introduce a lensless accommodation-capable holographic system based on a waveguide. Our system aims to expand the eye-box at the optimal viewing distance that provides the maximum FOV. We devised a formalized CGH algorithm based on bold assumption and two constraints and successfully performed numerical observation simulation. In optical experiments, accommodation-capable images with a maximum horizontal FOV of 7.0 degrees were successfully observed within an expanded eye-box of 9.18 mm at an optimal observation distance of 112 mm.

Holographic optical tweezers (HOT) is a programmable technique used for manipulation of microsized samples. In combination with computer-generation holography (CGH), a spatial light modulator reshapes the light distribution within the focal area of the optical tweezers. HOT can be used to realize real-time multiple-point manipulation in fluid, and this is useful in biological research. In this article, we summarize the HOT technique, discuss its recent developments, and present an overview of its biological applications.