Aberration correction based on a pre-correction convolutional neural network for light-field displays
Xunbo Yu, Hanyu Li, Xinzhu Sang, Xiwen Su, Xin Gao, Boyang Liu, Duo Chen, Yuedi Wang, Binbin Yan
Abstract
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.