In the integral imaging light field display, the introduction of a diffractive optical element (DOE) can solve the problem of limited depth of field of the traditional lens. However, the strong aberration of the DOE significantly reduces the final display quality. Thus, herein, an end-to-end joint optimization method for optimizing DOE and aberration correction is proposed. The DOE model is established using thickness as the variable, and a deep learning network is built to preprocess the composite image loaded on the display panel. The simulation results show that the peak signal to noise ratio value of the optimized image increases by 8 dB, which confirms that the end-to-end joint optimization method can effectively reduce the aberration problem.

3D光场显示技术因具有较大的观看视角、密集的观看视点而被研究学者们关注。分辨率是3D光场显示技术的一个重要参数，提升分辨率的方法较为复杂，因此研究学者们开始关注视觉分辨率。为了提高3D光场显示的视觉分辨率，提出了一种基于深度学习获取预处理基元图像阵列（PEIA）的方法。在光场显示的成像过程中，透镜的像差会使成像平面上形成弥散斑。弥散斑之间的交叠区域可以被视为新的视觉像素，并被用作额外的信息载体。一个分辨率增强的卷积神经网络（CNN）被用来从高分辨率基元图像阵列（HEIA）中获取PEIA，将PEIA加载到LCD上，经过透镜阵列的光学变换和定向扩散膜的扩散作用，呈现出具有视觉分辨率增强的3D光场显示图像。在实验中，通过使用PEIA、透镜阵列以及定向扩散膜，展示了一个具有70°视角的光场显示，并提高了视觉分辨率。