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

Lens aberrations degrade the image quality and limit the viewing angle of light-field displays. In the present study, an approach to aberration reduction based on a pre-correction convolutional neural network (CNN) is demonstrated. The pre-correction CNN is employed to transform the elemental image array (EIA) generated by a virtual camera array into a pre-corrected EIA (PEIA). The pre-correction CNN is built and trained based on the aberrations of the lens array. The resulting PEIA, rather than the EIA, is presented on the liquid crystal display. Via the optical transformation of the lens array, higher quality 3D images are obtained. The validity of the proposed method is confirmed through simulations and optical experiments. A 70-degree viewing angle light field display with the improved image quality is demonstrated. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

This study demonstrates a method of reducing lens aberrations and improving image quality in light-field displays using a pre-correction convolutional neural network. By transforming the elemental image array generated by a virtual camera array into a pre-corrected EIA, higher quality 3D images are achieved through optical transformation of the lens array. The proposed method is validated through simulations and optical experiments, resulting in a 70-degree viewing angle light field display with improved image quality.