改善硬件不良全息显示的物理信息学习模型  

Learning Model based on Physical Information for Improving Holographic Display with Poor Hardware

作　　者：杨屹森 匡登峰[1,2] Yang Yisen;Kuang Dengfeng(Institute of Modern Optics,College of Electronic Information and Optical Engineering,Nankai University,Tianjin 300350,China;Tianjin Key Laboratory of MicroScale Optical Information Science and Technology,Tianjin 300350,China)

机构地区：[1]南开大学电子信息与光学工程学院现代光学研究所,天津300350 [2]天津市微尺度光学信息技术科学重点实验室,天津300350

出　　处：《激光与光电子学进展》2024年第24期28-34,共7页Laser & Optoelectronics Progress

摘　　要：针对计算机生成全息术算法由理想和实际的光传输模型不匹配导致的实际重建图像质量下降的问题,提出一种简化的、具有物理信息的、基于学习的全息光传输模型。该模型可以显式地学习全息显示器的缺陷,灵活地应用于各种全息图优化算法,解决算法与实际光传输模型不匹配的问题。在未精调全息显示器原型中,所提模型的重建结果优于理想的全息光传输模型,能在不对光学元件的精细装配和激光光源的良好均匀性提出严格要求的情况下获得更高质量的全息重建图像。Aiming at the degradation of actual reconstructed image quality caused by the mismatch between ideal and practical optical transmission models in a computer-generated holography algorithm,this paper proposes a simplified learning-based holographic optical transmission model that uses physical information.The proposed model can explicitly learn the defects of holographic displays and can be flexibly used in various hologram optimization algorithms to solve the mismatch problem in optical transmission models.In the untuned holographic display prototype,reconstruction results of the proposed model are superior to those of ideal holographic light transmission model.Moreover,the proposed model can obtain high-quality holographic reconstruction images without strict requirements such as the fine assembly of optical elements and the good uniformity of laser light sources.

关 键 词：全息图 计算机生成全息术 机器学习 全息显示 全息光传输模型 

分 类 号：O438.1[机械工程—光学工程]