Holographic Maxwellian display based on complex-valued convolutional neural network for optical see-through near-eye display applications

Abstract

The holographic Maxwellian display, as a promising technique, offers a potential solution to the vergence–accommodation conflict in see-through near-eye displays for augmented reality applications. However, traditional holographic Maxwellian displays primarily rely on iterative or non-iterative algorithms, which face the challenge of balancing image quality and computational efficiency. To address this issue, we propose a lensless phase-only holographic Maxwellian display based on a complex-valued convolutional neural network algorithm. The complex-valued convolutional approach captures both the phase and amplitude information of light waves, enriching the holographic details and significantly improving the quality of the reconstructed images. Simulation results demonstrate that the quality of the reconstructed image can be significantly enhanced while maintaining high computational efficiency compared to conventional algorithms. Furthermore, by multiplying the phase hologram with a convergent spherical wave at the hologram plane, the virtual target image is focused on the viewer's pupil, ensuring a consistent perception of all-in-focus images at the pupil's location. To expand the size of the eyebox, multiple digital spherical waves were employed in the holographic Maxwellian display. Finally, experimental results validate that our proposed near-eye display system successfully generates see-through virtual images, effectively eliminating the vergence–accommodation conflict. The demonstrated capabilities of the proposed method underscore its considerable potential for applications in holographic near-eye displays.