Computational Coherent Imaging For Accommodation-Invariant Near-Eye Displays

We present a computational accommodation-invariant near-eye display, which relies on imaging with coherent light and utilizes static optics together with convolutional neural network-based preprocessing. The network and the display optics are co-optimized to obtain a depth-invariant display point sp...

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Bibliographic Details
Main Authors: Makinen, Jani, Sahin, Erdem, Akpinar, Ugur, Gotchev, Atanas
Format: Conference Proceeding
Language:English
Subjects:
Coherence
Coherent imaging
Computational near-eye displays
Integrated optics
Lighting
Neural networks
Optical design
Optical losses
Optics
Speckle
Training
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Description
Summary:We present a computational accommodation-invariant near-eye display, which relies on imaging with coherent light and utilizes static optics together with convolutional neural network-based preprocessing. The network and the display optics are co-optimized to obtain a depth-invariant display point spread function, and thus relieve the conflict between accommodation and ocular vergence cues that typically exists in conventional near-eye displays. We demonstrate through simulations that the computational near-eye display designed based on the proposed approach can deliver sharp images within a depth range of 3 diopters for an effective aperture (eyepiece) size of 10 mm. Thus, it provides a competitive alternative to the existing accommodation-invariant displays.
ISSN:2381-8549
DOI:10.1109/ICIP42928.2021.9506773