Real-Time High-Quality Computer-Generated Hologram Using Complex-Valued Convolutional Neural Network
Chongli Zhong, Xinzhu Sang, Binbin Yan, Hui Li, Duo Chen, Xiujuan Qin, Shuo Chen, Xiaoqian Ye
Abstract
Holographic displays are ideal display technologies for virtual and augmented reality because all visual cues are provided. However, real-time high-quality holographic displays are difficult to achieve because the generation of high-quality computer-generated hologram (CGH) is inefficient in existing algorithms. Here, complex-valued convolutional neural network (CCNN) is proposed for phase-only CGH generation. The CCNN-CGH architecture is effective with a simple network structure based on the character design of complex amplitude. A holographic display prototype is set up for optical reconstruction. Experiments verify that state-of-the-art performance is achieved in terms of quality and generation speed in existing end-to-end neural holography methods using the ideal wave propagation model. The generation speed is three times faster than HoloNet and one-sixth faster than Holo-encoder, and the Peak Signal to Noise Ratio (PSNR) is increased by 3 dB and 9 dB, respectively. Real-time high-quality CGHs are generated in 1920 × 1072 and 3840 × 2160 resolutions for dynamic holographic displays.