Holographic near-eye displays present true three-dimensional images with full monocular depth cues. In this paper, we propose a technique to expand the eyebox of the holographic near-eye displays. The base eyebox of the holographic near-eye displays is determined by the space bandwidth product of a spatial light modulator. The proposed technique replicates and stitches the base eyebox by the combined use of a holographic optical element and high order diffractions of the spatial light modulator, achieving horizontally and vertically expanded eyebox. An angular spectrum wrapping technique is also applied to alleviate image distortions observed at the boundaries between the replicated base eyeboxes.
We propose a lightguide-type super multi-view near-eye display that uses a digital micromirror device and a LED array. The proposed method presents three-dimensional images with a natural monocular depth cue using a compact combiner optics which consists of a thin lightguide and holographic optical elements (HOEs). Feasibility of the proposed method is verified by optical experiments which demonstrate monocular three-dimensional image presentation over a wide depth range. We also analyze the degradation of the image quality stemming from the spectral spread of the HOEs and show its reduction by a pre-compensation exploiting an adaptive moment estimation (Adam) optimizer.
Occlusion handling in computer-generated holography is of vast importance as it enhances depth information by presenting correct motion parallax of the 3D scene within the viewing angle. In this paper, we propose a computationally efficient occlusion handling technique based on a fully analytic mesh based computer generated holography. The proposed technique uses angular spectrum convolution that renders exact occlusion while preserving all other aspects of the fully analytic mesh based computer generated holography. The proposed method is computationally efficient as only a single convolution operation is required for each mesh without numerical propagation between the meshes. The proposed method is also exact as it performs the occlusion processing in the tilted mesh plane, being free from artifacts coming from orthographic spatial masking. The proposed method can be applied to the self and the mutual occlusions between the objects in the 3D scene. The computer simulated results show the feasibility of the proposed method.
In this paper, we investigate a learning-based complex field recovery technique of an object from its digital hologram. Most of the previous learning-based approaches first propagate the captured hologram to the object plane and then suppress the DC and conjugate noise in the reconstruction. To the contrary, the proposed technique utilizes a deep learning network to extract the object complex field in the hologram plane directly, making it robust to the object depth variations and well suited for three-dimensional objects. Unlike the previous approaches which concentrate on transparent biological samples having near-uniform amplitude, the proposed technique is applied to more general objects which have large amplitude variations. The proposed technique is verified by numerical simulations and optical experiments, demonstrating its feasibility.
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