Learning-based computer-generated holography (CGH) has shown remarkable promise to enable real-time holographic displays. Supervised CGH requires creating a large-scale dataset with target images and corresponding holograms. We propose a diffraction model-informed neural network framework (self-holo) for 3D phase-only hologram generation. Due to the angular spectrum propagation being incorporated into the neural network, the self-holo can be trained in an unsupervised manner without the need of a labeled dataset. Utilizing the various representations of a 3D object and randomly reconstructing the hologram to one layer of a 3D object keeps the complexity of the self-holo independent of the number of depth layers. The self-holo takes amplitude and depth map images as input and synthesizes a 3D hologram or a 2D hologram. We demonstrate 3D reconstructions with a good 3D effect and the generalizability of self-holo in numerical and optical experiments.

Among the important features of holographic displays are the wide viewing angles and the full color of the reconstructed images. The present work focuses on achievement of both features. We propose an increased-viewing-angle full-color holographic display using two tiled phase-only spatial light modulators (SLMs), a $4f$ concave mirrors system, and a temporal-spatial multiplexing method. The $4f$ optical system consists of two concave mirrors and serves to increase the viewing angle. A temporal-spatial multiplexing synchronization control (TSMSC) method is developed to achieve a full-color image and to remove the color crosstalk of the image. We calculate RGB phase-only holograms of a computer-generated color pyramid by using a slice-based Fresnel diffraction algorithm. The experimental results indicate that the proposed display system is feasible to reconstruct a full-color holographic 3D image with a viewing angle of 12.8°, which is about 3.8 times wider than the viewing angle formed by a single SLM.

Learning-based computer-generated holography (CGH) has demonstrated great potential in enabling real-time, high-quality holographic displays. However, most existing learning-based algorithms still struggle to produce high-quality holograms, due to the difficulty of convolutional neural networks (CNNs) in learning cross-domain tasks. Here, we present a diffraction model-driven neural network (Res-Holo) using hybrid domain loss for phase-only hologram (POH) generation. Res-Holo utilizes the weights of the pretrained ResNet34 as the initialization during the encoder stage of the initial phase prediction network to extract more generic features and also to help prevent overfitting. Also, frequency domain loss is added to further constrain the information that the spatial domain loss is insensitive. The peak signal-to-noise ratio (PSNR) of the reconstructed image is improved by 6.05 dB using hybrid domain loss compared to using spatial domain loss alone. Simulation results show that the proposed Res-Holo can generate high-fidelity 2 K resolution POHs with an average PSNR of 32.88 dB at 0.014 seconds/frame on the DIV2K validation set. Both monochrome and full-color optical experiments show that the proposed method can effectively improve the quality of reproduced images and suppress image artifacts.

Existing computational holographic displays often suffer from limited reconstruction image quality mainly due to ill-conditioned optics hardware and hologram generation software. In this Letter, we develop an end-to-end hardware-in-the-loop approach toward high-quality hologram generation for holographic displays. Unlike other hologram generation methods using ideal wave propagation, ours can reduce artifacts introduced by both the light propagation model and the hardware setup, in particular non-uniform illumination. Experimental results reveal that, compared with classical computer-generated hologram algorithm counterparts, better quality of holographic images can be delivered without a strict requirement on both the fine assembly of optical components and the good uniformity of laser sources.

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

Depth-division multiplexing (DDM) is a common method for full-color hologram generation. However, this method will result in uneven image-quality levels at different color channels of the original color image. In this paper, the DDM method with dynamic compensation is proposed for a full-color holographic display. Three monochromatic images of red (R), green (G), and blue (B) channels from the original color image are placed orderly at different positions (object planes) of the same optical axis; then, the complex amplitudes of the three object planes are iteratively updated in a designed order when a laser wavefront propagates between object planes and the hologram plane. In the iterative process, a dynamic compensation factor is added to the complex amplitude of each object plane, which can effectively balance the quality level of the reconstructed image in each color channel. As a result, the image quality of a full-color object is improved. Numerical simulation and optical experiments are carried out to verify the method’s feasibility.