Zhou Ruyi, Wei Chenxiao, Ma Haowen, Cao Shuo, Ahmad Munzza, Li Chao, Li Jingnan, Sun Yutong, Wang Yongtian, Liu Juan
Opt Express. 2023 Nov 6;31(23):38146-38164. doi: 10.1364/OE.503056.
In lens-based display systems, lens aberrations and depth of field (DoF) limitation often lead to blurring and distortion of reconstructed images; Meanwhile, expanding the display DoF will face a trade-off between horizontal resolution and axial resolution, restricting the achievement of high-resolution and large DoF three-dimensional (3D) displays. To overcome these constraints and enhance the DoF and resolution of reconstructed scenes, we propose a DoF expansion method based on diffractive optical element (DOE) optimization and image pre-correction through a convolutional neural network (CNN). This method applies DOE instead of the conventional lens and optimizes DOE phase distribution using the Adam algorithm, achieving depth-invariant and concentrated point spread function (PSF) distribution throughout the entire DoF range; Simultaneously, we utilize a CNN to pre-correct the original images and compensate for the image quality reduction introduced by the DOE. The proposed method is applied to a practical integral imaging system, we effectively extend the DoF of the DOE to 400 mm, leading to a high-resolution 3D display in multiple depth planes. To validate the effectiveness and practicality of the proposed method, we conduct numerical simulations and optical experiments.
在基于透镜的显示系统中，透镜像差和景深（DoF）限制常常导致重建图像的模糊和失真；与此同时，扩大显示景深将面临水平分辨率和轴向分辨率之间的权衡，限制了高分辨率和大景深三维（3D）显示的实现。为了克服这些限制并提高重建场景的景深和分辨率，我们提出了一种基于衍射光学元件（DOE）优化和通过卷积神经网络（CNN）进行图像预校正的景深扩展方法。该方法应用DOE代替传统透镜，并使用Adam算法优化DOE相位分布，在整个景深范围内实现深度不变且集中的点扩散函数（PSF）分布；同时，我们利用CNN对原始图像进行预校正，并补偿DOE引入的图像质量下降。将所提出的方法应用于实际的积分成像系统，我们有效地将DOE的景深扩展到400毫米，从而在多个深度平面上实现高分辨率3D显示。为了验证所提出方法的有效性和实用性，我们进行了数值模拟和光学实验。
