Exploring angular-steering illumination-based eyebox expansion for holographic displays.

Holography represents an enabling technology for next-generation virtual and augmented reality systems. However, it remains challenging to achieve both wide field of view and large eyebox at the same time for holographic near-eye displays, mainly due to the essential étendue limitation of existing hardware. In this work, we present an approach to expanding the eyebox for holographic displays without compromising their underlying field of view. This is achieved by utilizing a compact 2D steering mirror to deliver angular-steering illumination beams onto the spatial light modulator in alignment with the viewer's eye movements. To facilitate the same image for the virtual objects perceived by the viewer when the eye moves, we explore an off-axis computational hologram generation scheme. Two bench-top holographic near-eye display prototypes with the proposed angular-steering scheme are developed, and they successfully showcase an expanded eyebox up to 8 mm × 8 mm for both VR- and AR-modes, as well as the capability of representing multi-depth holographic images.

Diffraction model-driven neural network trained using hybrid domain loss for real-time and high-quality computer-generated holography.

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.

Investigating learning-empowered hologram generation for holographic displays with ill-tuned hardware.

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.

Diffraction model-informed neural network for unsupervised layer-based computer-generated holography.

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.

Computer-generated full-color phase-only hologram using a multiplane iterative algorithm with dynamic compensation.

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.