Patch scanning displays: spatiotemporal enhancement for displays

Learned holographic light transport: invited

Computer-generated holography algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches.

Volumetric Head-Mounted Display With Locally Adaptive Focal Blocks

A commercial head-mounted display (HMD) for virtual reality (VR) presents three-dimensional imagery with a fixed focal distance. The VR HMD with a fixed focus can cause visual discomfort to an observer. In this article, we propose a novel design of a compact VR HMD supporting near-correct focus cues over a wide depth of field (from 18 cm to optical infinity). The proposed HMD consists of a low-resolution binary backlight, a liquid crystal display panel, and focus-tunable lenses. In the proposed system, the backlight locally illuminates the display panel that is floated by the focus-tunable lens at a specific distance. The illumination moment and the focus-tunable lens' focal power are synchronized to generate focal blocks at the desired distances. The distance of each focal block is determined by depth information of three-dimensional imagery to provide near-correct focus cues. We evaluate the focus cue fidelity of the proposed system considering the fill factor and resolution of the backlight. Finally, we verify the display performance with experimental results.

Compact tomographic near-eye display using a MEMS scanning mirror

We propose a compact tomographic near-eye display by combining a micro-electro-mechanical systems (MEMS) scanning mirror device, focus tunable lens, and a single light-emitting diode source. A holographic optical element was used to elaborately focus the light source into the MEMS scanning mirror while providing further miniaturization. We implemented a drastically downsized multifocal display system that expresses a depth range of 4.8 D with eight focal planes by synchronizing these modules. Furthermore, the planar images are optimized to achieve correct retinal scenes at each accommodation state. The simulated and experimental results verify that the suggested near-eye display system can provide three-dimensional virtual images while showing physical feasibility.

Beaming Displays

Existing near-eye display designs struggle to balance between multiple trade-offs such as form factor, weight, computational requirements, and battery life. These design trade-offs are major obstacles on the path towards an all-day usable near-eye display. In this work, we address these trade-offs by, paradoxically, removing the display from near-eye displays. We present the beaming displays, a new type of near-eye display system that uses a projector and an all passive wearable headset. We modify an off-the-shelf projector with additional lenses. We install such a projector to the environment to beam images from a distance to a passive wearable headset. The beaming projection system tracks the current position of a wearable headset to project distortion-free images with correct perspectives. In our system, a wearable headset guides the beamed images to a user's retina, which are then perceived as an augmented scene within a user's field of view. In addition to providing the system design of the beaming display, we provide a physical prototype and show that the beaming display can provide resolutions as high as consumer-level near-eye displays. We also discuss the different aspects of the design space for our proposal.

Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements

The subpixel displacement estimation is an important step to calculation of the displacement between two digital images in optics and image processing. Digital image correlation (DIC) is an effective method for measuring displacement due to its high accuracy. Various DIC algorithms to compare images and to obtain displacement have been implemented. However, there are some drawbacks to DIC. It can be computationally expensive when processing a sequence of continuously deformed images. To simplify the subpixel displacement estimation and to explore a different measurement scheme, a convolutional neural network with a transfer learning based subpixel displacement measurement method (CNN-SDM) is proposed in this paper. The basic idea of the method is to compare images of an object decorated with speckle patterns before and after deformation by CNN, and thereby to achieve a coarse-to-fine subpixel displacement estimation. The proposed CNN is a classification model consisting of two convolutional neural networks in series. The results of simulated and real experiments are shown that the proposed CNN-SDM method is feasibly effective for subpixel displacement measurement due its high efficiency, robustness, simple structure and few parameters.

Correcting the Proximity Effect in Nanophotonic Phased Arrays

Thermally modulated Nanophotonic Phased Arrays (NPAs) can be used as phase-only holographic displays. Compared to the holographic displays based on Liquid Crystal on Silicon Spatial Light Modulators (LCoS SLMs), NPAs have the advantage of integrated light source and high refresh rate. However, the formation of the desired wavefront requires accurate modulation of the phase which is distorted by the thermal proximity effect. This problem has been largely overlooked and existing approaches to similar problems are either slow or do not provide a good result in the setting of NPAs. We propose two new algorithms based on the iterative phase retrieval algorithm and the proximal algorithm to address this challenge. We have carried out computational simulations to compare and contrast various algorithms in terms of image quality and computational efficiency. This work is going to benefit the research on NPAs and enable the use of large-scale NPAs as holographic displays.