首页|Non-convex optimization for inverse problem solving in computer-generated holography
Non-convex optimization for inverse problem solving in computer-generated holography
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Computer-generated holography is a promising technique that modulates user-defined wavefronts with digital holograms.Computing appropriate holograms with faithful reconstructions is not only a problem closely related to the fundamental basis of holography but also a long-standing challenge for researchers in general fields of optics.Finding the exact solution of a desired hologram to reconstruct an accurate target object constitutes an ill-posed inverse problem.The general practice of single-diffraction computation for synthesizing holograms can only provide an approximate answer,which is subject to limitations in numerical implementation.Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints,frameworks,and initializations.Herein,we overview the optimization algorithms applied to computer-generated holography,incorporating principles of hologram synthesis based on alternative projections and gradient descent methods.This is aimed to provide an underlying basis for optimized hologram generation,as well as insights into the cutting-edge developments of this rapidly evolving field for potential applications in virtual reality,augmented reality,head-up display,data encryption,laser fabrication,and metasurface design.
Xiaomeng Sui、Zehao He、Daping Chu、Liangcai Cao
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Department of Precision Instruments,Tsinghua University,Beijing 100084,China
Department of Engineering,Centre for Photonic Devices and Sensors,University of Cambridge,9 JJ Thomson Avenue,Cambridge CB3 0FA,UK
Cambridge University Nanjing Centre of Technology and Innovation,23 Rongyue Road,Jiangbei New Area,Nanjing 210000,China
National Science Foundation of ChinaTsinghua University Initiative Scientific Research Program
6203500320193080075as well as the Cambridge Tsinghua Joint Research Initiative
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