Dual convolutional neural network for aberration pre-correction and image quality enhancement in integral imaging display

This paper proposes a method that utilizes a dual neural network model to address the challenges posed by aberration in the integral imaging microlens array (MLA) and the degradation of 3D image quality. The approach involves a cascaded dual convolutional neural network (CNN) model designed to handle aberration pre-correction and image quality restoration tasks. By training these models end-to-end, the MLA aberration is corrected effectively and the image quality of integral imaging is enhanced. The feasibility of the proposed method is validated through simulations and optical experiments, using an optimized, high-quality pre-corrected element image array (EIA) as the image source for 3D display. The proposed method achieves high-quality integral imaging 3D display by alleviating the contradiction between MLA aberration and 3D image resolution reduction caused by system noise without introducing additional complexity to the display system.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

This paper proposes a method that utilizes a dual neural network model to address the challenges posed by aberration in the integral imaging microlens array (MLA) and the degradation of 3D image quality. The proposed method achieves high-quality integral imaging 3D display by effectively correcting MLA aberration and enhancing image quality.