Physics-aware cross-domain fusion aids learning-driven computer-generated holography  

作　　者：GANZHANGQIN YUAN MI ZHOU FEI LIU MU KU CHEN KUI JIANG YIFAN PENG ZIHAN GENG 

机构地区：[1]Institute of Data and Information,Tsinghua Shenzhen International Graduate School,Tsinghua University,Shenzhen 518071,China [2]School of Optoelectronic Engineering,Xidian University,Xi’an 710071,China [3]Department of Electrical Engineering,City University of Hong Kong,Hong Kong SAR,China [4]School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China [5]Department of Electrical and Electronic Engineering,The University of Hong Kong,Hong Kong SAR,China [6]Department of Computer Science,The University of Hong Kong,Hong Kong SAR,China

出　　处：《Photonics Research》2024年第12期2747-2756,共10页光子学研究(英文版)

基　　金：National Natural Science Foundation of China(62305184);Basic and Applied Basic Research Foundation of Guangdong Province(2023A1515012932);Science,Technology and Innovation Commission of Shenzhen Municipality(WDZC20220818100259004);Research Grants Council of the Hong Kong Special Administrative Region,China(C5031-22G,CityU11300123,CityU11310522);Guangdong Provincial Department of Science and Technology(2020B1515120073);City University of Hong Kong(9610628);Research Grants Council of Hong Kong(ECS 27212822).

摘　　要：The rapid advancement of computer-generated holography has bridged deep learning with traditional optical principles in recent years.However,a critical challenge in this evolution is the efficient and accurate conversion from the amplitude to phase domain for high-quality phase-only hologram(POH)generation.Existing computational models often struggle to address the inherent complexities of optical phenomena,compromising the conversion process.In this study,we present the cross-domain fusion network(CDFN),an architecture designed to tackle the complexities involved in POH generation.The CDFN employs a multi-stage(MS)mechanism to progressively learn the translation from amplitude to phase domain,complemented by the deep supervision(DS)strategy of middle features to enhance task-relevant feature learning from the initial stages.Additionally,we propose an infinite phase mapper(IPM),a phase-mapping function that circumvents the limitations of conventional activation functions and encapsulates the physical essence of holography.Through simulations,our proposed method successfully reconstructs high-quality 2K color images from the DIV2K dataset,achieving an average PSNR of 31.68 dB and SSIM of 0.944.Furthermore,we realize high-quality color image reconstruction in optical experiments.The experimental results highlight the computational intelligence and optical fidelity achieved by our proposed physics-aware cross-domain fusion.

关 键 词：COMPUTER CROSS HOLOGRAPHY 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O438.1[自动化与计算机技术—计算机科学与技术]