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Aberration correction based on a pre-correction convolutional neural network for light-field displays.

Xunbo Yu, Hanyu Li, Xinzhu Sang

|April 6, 2021

Summary

This study introduces a convolutional neural network (CNN) to correct lens aberrations in light-field displays. The method enhances 3D image quality and expands the viewing angle for immersive visual experiences.

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Area of Science:

Optics
Computer Vision
Display Technology

Background:

Lens aberrations significantly degrade image quality and limit viewing angles in light-field displays.
Current methods for aberration correction are often complex or insufficient for achieving high-fidelity 3D imaging.

Purpose of the Study:

To develop and demonstrate a novel approach for reducing lens aberrations in light-field displays using a pre-correction convolutional neural network (CNN).
To improve the overall image quality and expand the viewing angle of light-field displays.

Main Methods:

A pre-correction CNN was designed and trained to transform elemental image arrays (EIAs) into pre-corrected EIAs (PEIAs) based on specific lens array aberrations.
The PEIA was then displayed on a liquid crystal display, utilizing the optical properties of the lens array for image reconstruction.

Main Results:

Simulations and optical experiments confirmed the effectiveness of the proposed CNN-based pre-correction method.
A light-field display with a 70-degree viewing angle and significantly improved image quality was successfully demonstrated.

Conclusions:

The CNN-based pre-correction technique offers an effective solution for mitigating lens aberrations in light-field displays.
This approach enables the creation of higher quality 3D images with wider viewing angles, advancing light-field display technology.