Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Aberration pre-correction for a simple optical system of HMDs

Not Accessible

Your library or personal account may give you access

Abstract

Head-mounted displays (HMDs) are becoming increasingly popular as a crucial component of virtual reality (VR). However, contemporary HMDs enforce a simple optical structure due to their constrained form factor, which impedes the use of multiple lens elements that can reduce aberrations in general. As a result, they introduce severe aberrations and imperfections in optical imagery, causing visual fatigue and degrading the immersive experience of being present in VR. To address this issue without modifying the hardware system, we present a novel, to the best of our knowledge, software-driven approach that compensates for the aberrations in HMDs in real time. Our approach involves pre-correction that deconvolves an input image to minimize the difference between its after-lens image and the ideal image. We characterize the specific wavefront aberration and point spread function (PSF) of the optical system using Zernike polynomials. To achieve higher computational efficiency, we improve the conventional deconvolution based on hyper-Laplacian prior by adopting a regularization constraint term based on L2 optimization and the input-image gradient. Furthermore, we implement our solution entirely on a graphics processing unit (GPU) to ensure constant and scalable real-time performance for interactive VR. Our experiments evaluating our algorithm demonstrate that our solution can reliably reduce the aberration of the after-lens images in real time.

© 2024 Optica Publishing Group

Full Article  |  PDF Article
More Like This
Simplified design method for optical imaging systems based on aberration characteristics of optical-digital joint optimization

Yuanhang Wang, Xing Zhong, Zheng Qu, Qixiang Gao, Lei Li, and Chaoli Zeng
Appl. Opt. 63(4) 1066-1078 (2024)

4-K-resolution minimalist optical system design based on deep learning

Dexiao Meng, Yan Zhou, and Jian Bai
Appl. Opt. 63(4) 917-926 (2024)

Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging

Chang Qiao, Haoyu Chen, Run Wang, Tao Jiang, Yuwang Wang, and Dong Li
Photon. Res. 12(3) 474-484 (2024)

Data availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (12)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (4)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (18)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.