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透过散射介质光学成像技术的研究进展

中国激光 2024年第11期51卷 248-275页

作者：卞耀明司徒国海中国科学院上海光学精密机械研究所信息光学与光电技术实验室上海201800 中国科学院大学材料科学与光电技术学院北京100049 国科大杭州高等研究院物理与光电工程学院浙江杭州310024

视觉是人类获取信息的重要手段,因此获取视觉信息的成像技术是医学检测、深海探测、国民安防中的主要技术之一。但是当成像链路中存在散射介质的时候,很多场景下的成像深度受限且质量受损,例如生物组织、浑浊水体、大气雨雾等。散射成... 详细信息

视觉是人类获取信息的重要手段,因此获取视觉信息的成像技术是医学检测、深海探测、国民安防中的主要技术之一。但是当成像链路中存在散射介质的时候,很多场景下的成像深度受限且质量受损,例如生物组织、浑浊水体、大气雨雾等。散射成像作为解决这类问题的关键方法,针对不同的散射成像场景,按照是否采用主动光照明可以分为主动和被动两种不同的成像模式。在不同模式下,根据散射光的形成原理和物理建模,综述了提取弹道光和利用散射光的散射成像技术,并介绍了深度学习在两种模式下的相关应用实例。最后,从前端光学调制和后端算法处理两个方向的发展趋势,展望了散射成像技术的突破路线。

关键词：散射成像主动模式被动模式提取弹道光利用散射光大气散射模型

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Deep holography

Light(Advanced Manufacturing) 2022年第2期3卷 100-122页

作者： guohai situ Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China

With the explosive growth of mathematical optimization and computing hardware,deep neural networks(DNN)have become tremendously powerful tools to solve many challenging problems in various fields,ranging from decision making to computational imaging and *** this manuscript,I focus on the prosperous interactions between DNN and *** the one hand,DNN has been demonstrated to be in particular proficient for holographic reconstruction and computer-generated holography almost in every *** the other hand,holography is an enabling tool for the optical implementation of DNN the other way around owing to the capability of interconnection and light speed processing in *** purpose of this article is to give a comprehensive literature review on the recent progress of deep holography,an emerging interdisciplinary research field that is mutually inspired by holography and DNN.I first give a brief overview of the basic theory and architectures of DNN,and then discuss some of the most important progresses of deep holography.I hope that the present unified exposition will stimulate further development in this promising and exciting field of research.

关键词： Deep learning deep neural networks digital holography computer-generated hologram optical neural networks

Image encryption using spatial nonlinear optics

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eLight 2022年第1期2卷 32-41页

作者： Junfeng Hou guohai situ Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences201800 ShanghaiChina Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences310024 HangzhouChina

Optical technologies have been widely used in information security owing to its parallel and high-speed processing ***,the most critical problem with current optical encryption techniques is that the cyphertext is linearly related with the plaintext,leading to the possibility that one can crack the system by solving a set of linear equations with only two cyphertext from the same encryption *** efforts have been taken in the last decade to resolve the linearity issue,but none of these offers a true nonlinear *** by the recent advance in spatial nonlinear optics,here we demonstrate a true nonlinear optical encryption *** show that,owing to the self-phase modulation effect of the photorefractive crystal,the proposed nonlinear optical image encryption technique is robust against the known plaintext attack based on phase *** opens up a new avenue for optical encryption in the spatial nonlinear domain.

关键词： Optical encryption Spatial nonlinear optics Self-defocusing

Far-field super-resolution ghost imaging with a deep neural network constraint

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Light(Science & Applications) 2022年第1期11卷 27-37页

作者： Fei Wang Chenglong Wang Mingliang Chen Wenlin Gong Yu Zhang Shensheng Han guohai situ Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China CAS Center for Excellence in Ultra-intense Laser Science Shanghai 201800China

Ghost imaging(GI)facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote ***,GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image,imposing a practical limit for its *** we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural *** resulting hybrid neural network does not need to pre-train on any dataset,and allows the reconstruction of a far-field image with the resolution beyond the diffraction ***,the physical model imposes a constraint to the network output,making it effectively *** experimentally demonstrate the proposed GI technique by imaging a flying drone,and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling *** believe that this study provides a new framework for GI,and paves a way for its practical applications.

关键词： neural ghost Ghost

Lensless complex amplitude demodulation based on deep learning in holographic data storage

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Opto-Electronic Advances 2023年第3期6卷 42-56页

作者： Jianying Hao Xiao Lin Yongkun Lin Mingyong Chen Ruixian Chen guohai situ Hideyoshi Horimai Xiaodi Tan College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhou 350117China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China HolyMine Corporation 2032-2-301 OokaNumazuShizuoka 410-0022Japan

To increase the storage capacity in holographic data storage(HDS),the information to be stored is encoded into a complex *** and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout in *** this study,we proposed a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level *** analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages,the inverse problem was decomposed into two backward operators denoted by two convolutional neural networks(CNNs)to demodulate amplitude and phase *** experimental system is simple,stable,and robust,and it only needs a single diffraction image to realize the direct demodulation of both amplitude and *** our investigation,this is the first time in HDS that multilevel complex amplitude demodulation is achieved experimentally from one diffraction intensity image without iterations.

关键词： holographic data storage complex amplitude demodulation deep learning computational imaging

Phase imaging with an untrained neural network

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Light(Science & Applications) 2020年第1期9卷 1258-1264页

作者： Fei Wang Yaoming Bian Haichao Wang Meng Lyu Giancarlo Pedrini Wolfgang Osten George Barbastathis guohai situ Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences201800 ShanghaiChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences100049 BeijingChina Institut für Technische Optik Universität StuttgartPfaffenwaldring 970569 StuttgartGermany Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139-4301USA Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences310024 HangzhouChina

Most of the neural networks proposed so far for computational imaging(CI)in optics employ a supervised training strategy,and thus need a large training set to optimize their weights and *** aside the requirements of environmental and system stability during many hours of data acquisition,in many practical applications,it is unlikely to be possible to obtain sufficient numbers of ground-truth images for ***,we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image *** most significant advantage of the resulting physics-enhanced deep neural network(PhysenNet)is that it can be used without training beforehand,thus eliminating the need for tens of thousands of labeled *** take single-beam phase imaging as an example for *** experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object,and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical *** opens up a new paradigm of neural network design,in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

关键词： neural network eventually

Single-pixel imaging using physics enhanced deep learning

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Photonics Research 2022年第1期10卷 104-110页

作者： FEI WANG CHENGLONG WANG CHENJIN DENG SHENSHENG HAN guohai situ Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China

Single-pixel imaging(SPI) is a typical computational imaging modality that allows two-and three-dimensional image reconstruction from a one-dimensional bucket signal acquired under structured *** is in particular of interest for imaging under low light conditions and in spectral regions where good cameras are ***,the resolution of the reconstructed image in SPI is strongly dependent on the number of measurements in the temporal ***-driven deep learning has been proposed for high-quality image reconstruction from a undersampled bucket *** the generalization issue prohibits its practical *** we propose a physics-enhanced deep learning approach for *** blending a physics-informed layer and a model-driven fine-tuning process,we show that the proposed approach is generalizable for image *** implement the proposed method in an in-house SPI system and an outdoor single-pixel LiDAR system,and demonstrate that it outperforms some other widespread SPI algorithms in terms of both robustness and *** proposed method establishes a bridge between data-driven and model-driven algorithms,allowing one to impose both data and physics priors for inverse problem solvers in computational imaging,ranging from remote sensing to microscopy.

关键词： SPI image illumination

Learning-based lensless imaging through optically thick scattering media

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Advanced Photonics 2019年第3期1卷 29-38页

作者： Meng Lyu Hao Wang Guowei Li Shanshan Zheng guohai situ Chinese Academy of Sciences Shanghai Institute of Optics and Fine MechanicsShanghaiChina University of Chinese Academy of Sciences Center for Materials Science and Optoelectronics EngineeringBeijingChina

The problem of imaging through thick scattering media is encountered in many disciplines of science,ranging from mesoscopic physics to *** become diffusive after propagating through a scattering medium with an optical thickness of over 10 times the scattering mean free *** a result,no image but only noise-like patterns can be directly *** propose a hybrid neural network for computational imaging through such thick scattering media,demonstrating the reconstruction of image information from various targets hidden behind a white polystyrene slab of 3 mm in thickness or 13.4 times the scattering mean free *** also demonstrate that the target image can be retrieved with acceptable quality from a very small fraction of its scattered pattern,suggesting that the speckle pattern produced in this way is highly *** leads to a profound question of how the information of the target being encoded into the speckle is to be addressed in future studies.

关键词： imaging through scattering media deep learning neural network computational imaging

Wide-spectrum optical synthetic aperture imaging via spatial intensity interferometry

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Opto-Electronic Advances 2023年第12期6卷 27-35页

作者： Chunyan Chu Zhentao Liu Mingliang Chen Xuehui Shao guohai situ Yuejin Zhao Shensheng Han Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology Beijing 100081China School of Optics and Photonics Beijing Institute of TechnologyBeijing 100081China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China National Laboratory of Aerospace Intelligent Control Technology Beijing 100089China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China.

High resolution imaging is achieved using increasingly larger apertures and successively shorter *** aperture synthesis is an important high-resolution imaging technology used in *** long baseline amplitude interferometry is susceptible to uncontrollable phase fluctuations,and the technical difficulty increases rapidly as the wavelength *** intensity interferometry inspired by HBT experiment is essentially insensitive to phase fluctuations,but suffers from a narrow spectral bandwidth which results in a lack of effective *** this study,we propose optical synthetic aperture imaging based on spatial intensity *** not only realizes diffraction-limited optical aperture synthesis in a single shot,but also enables imaging with a wide spectral bandwidth,which greatly improves the optical energy efficiency of intensity *** this method is insensitive to the optical path difference between the *** and experiments present optical aperture synthesis diffraction-limited imaging through spatial intensity interferometry in a 100 nm spectral width of visible light,whose maximum optical path difference between the sub-apertures reaches 69λ.This technique is expected to provide a solution for optical aperture synthesis over kilometer-long baselines at optical wavelengths.

关键词： optical synthetic aperture imaging ghost imaging intensity interferometry