A holographic imaging device and method realizes both a transmission type and a reflection type, and also realizes a long working distance wide field of view or ultra-high resolution. Object light emitted from an object, sequentially illuminated with parallel illumination light whose incident direction is changed, is recorded on a plurality of object light holograms for each incident direction using off-axis spherical wave reference light. The reference light is recorded on a reference light hologram using in-line spherical wave reference light being in-line with the object light. An object light wave hologram and its spatial frequency spectrum at the object position are generated for each incident direction using each hologram. A synthetic spectrum which occupies a wider frequency space is generated by matching each spectrum in the overlapping area, and a synthetic object light wave hologram with increased numerical aperture is obtained thereby.

An augmented reality display device (such as a head mounted device) includes a partially transparent and partially reflective lens, a laser light source, a radio frequency source, a display controller, an acousto-optical modulator, and a microelectromechanical (MEMS) device. The laser light source generates light. The radio frequency (RF) source generates a RF signal. The display controller generates a synchronization signal. The acousto-optical modulator receives at least a portion of the light, modulates the light based on the RF signal, and provides modulated light. The MEMS device receives the synchronization signal from the display controller and reflects the modulated light towards the partially transparent and partially reflective lens. The MEMS device determines a direction in which the modulated light reflects based on the synchronization signal and the partially transparent and partially reflective lens reflecting the modulated laser light towards an eye of a user of the augmented realty display device.

Provided are a webserver-based video content production system and method for printing a holographic stereogram photo. According to the webserver-based video content production system and method, an encoding process is performed on image data under a web environment to prevent data loss in a network communication process, and then the encoded image data is transmitted to a webserver. The webserver decodes the encoded image data and performs a second-half task (postprocessing) on the decoded image data using deep learning such that a high-quality hologram photo can be printed within a relatively short time without any unnecessary process.

A display device includes a display panel and a window member provided on the display panel. The window member includes a plastic window, a first hard coating layer provided on the plastic window, and a second hard coating layer provided on the first hard coating layer. A surface water contact angle of the second hard coating layer may be in a range of 90 degrees to 117 degrees.

Relay systems and methods are operable to redirect light corresponding to a light field or holographic object such that imagery generated by a light field or other display is perceived by a viewer without having to address the display itself.

Display devices can be used to provide display functionality in dynamic autostereoscopic displays. One or more display devices are coupled to one or more appropriate computing devices. These computing devices control delivery of autostereoscopic image data to the display devices. A lens array coupled to the display devices, e.g., directly or through some light delivery device, provides appropriate conditioning of the autostereoscopic image data so that users can view dynamic autostereoscopic images. Methods and systems for calibrating a hogel display are also described, including generating calibration hogel data corresponding to a calibration pattern; generating a hogel light field from the calibration hogel data; detecting the hogel light field; and determining calibration data by analyzing a set of hogel properties in response to detecting the hogel light field. The methods and systems may further include generating a calibrated hogel light field by generating calibrated hogel data using the calibration data.

To reduce an influence of stray light and stably record/reproduce high-quality data in holographic recording/reproduction. A holographic memory device includes an optical system that guides a reference beam to an optical information recording medium at a desired angle of incidence, a control part that controls the angle of incidence of the reference beam generated in the optical system, and a lens part that images the reference beam in a desired position of the optical information recording medium. Further, at least a first light beam at a first angle and a second light beam at a second angle different from the first angle are output from the optical element, and the optical element is provided so that the first light beam may propagate within an effective diameter of the lens part and the second light beam may propagate to an outside of the effective diameter of the lens part.

An example hologram device may include a target holographic view of an encoded holographic image representing an alignment target; and a plurality of non-target holographic views of the encoded holographic image, each non-target holographic view indicating a position relative to the target holographic view.

Devices utilizing holographic 4D plenoptic capture and display technologies to generate a light field function to provide glasses-less vision correction for observers with imperfect vision, and to project an image according to the generated light field function, and methods for calibrating a four-dimensional light field for a user with an uncorrected visual acuity.

An all-optical Diffractive Deep Neural Network (D.sup.2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D.sup.2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D.sup.2NNs. In alternative embodiments, the all-optical D.sup.2NN is used as a front-end in conjunction with a trained, digital neural network back-end.