摘要
传统的纯相位全息成像方法,大多数依赖于高强度的迭代,耗费时间长,成像质量不高,针对此问题,提出了一种深度学习与分层角谱结合的纯相位全息图生成算法,在快速生成全息图的同时提高了全息图再现质量。通过LeNet网络结构预测三维物体的复振幅信息,降低了计算量,采用精确的角谱算法生成三维物体的高质量纯相位全息图。通过仿真实验证明该算法的可行性,并有效提高了重建图像的质量。
Traditional phase-only holographic imaging methods rely on high-intensity iteration, which is time-consuming, and the imaging quality is not high. To address this issue, a phase-only hologram generation algorithm based on depth learning and angular-spectrum layer-oriented, which can generate holograms quickly and improve the quality of hologram reconstruction, is proposed. The LeNet network structure predicts the complex amplitude information of three-dimensional objects, which reduces the amount of calculation. The accurate angular-spectrum algorithm creates a high-quality phase-only hologram of a three-dimensional object. The simulation results show that the algorithm is feasible and the quality of the reconstructed image is effectively improved.
作者
孙骁
韩超
Sun Xiao;Han Chao(Key Laboratory of Advanced Perception and Intelligent Control of Highend Equipment,Ministry of Education,Anhui Polytechnic University,Wuhu,Anhui 241000,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第4期45-53,共9页
Laser & Optoelectronics Progress
基金
安徽工程大学检测技术与节能装置安徽省重点实验室开放基金(DTESD2020A06)。
关键词
全息
深度学习
分层角谱
纯相位全息图
三维显示
holography
deep learning
angular-spectrum layer-oriented
phase-only hologram
three-dimensional display
