Showing papers by "Guohai Situ published in 2022"
TL;DR: In this paper , a far-field super-resolution Ghost Imaging (GI) technique was proposed that incorporates the physical model for GI image formation into a deep neural network, and the resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a farfield image with the resolution beyond the diffraction limit.
Abstract: Abstract 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 sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The 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 limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We 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 ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.
139 citations
TL;DR: In this paper , a far-field super-resolution Ghost Imaging (GI) technique was proposed that incorporates the physical model for GI image formation into a deep neural network, and the resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a farfield image with the resolution beyond the diffraction limit.
Abstract: Abstract 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 sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The 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 limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We 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 ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.
110 citations
23 citations
TL;DR: In this article , the authors proposed a nonlinear optical image encryption technique, which is robust against the known plaintext attack based on phase retrieval, and showed that the self-phase modulation effect of the photorefractive crystal is responsible for the robustness of the proposed scheme.
Abstract: Abstract Optical technologies have been widely used in information security owing to its parallel and high-speed processing capability. However, 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 machine. Many efforts have been taken in the last decade to resolve the linearity issue, but none of these offers a true nonlinear solution. Inspired by the recent advance in spatial nonlinear optics, here we demonstrate a true nonlinear optical encryption technique. We 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 retrieval. This opens up a new avenue for optical encryption in the spatial nonlinear domain.
18 citations
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TL;DR: In this article , a comprehensive literature review on the recent progress of deep holography, an emerging interdisciplinary research field that is mutually inspired by holographic and deep neural networks is presented.
Abstract: 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 holography. In this manuscript, I focus on the prosperous interactions between DNN and holography. On the one hand, DNN has been demonstrated to be in particular proficient for holographic reconstruction and computer-generated holography almost in every aspect. On 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 parallel. The 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.
14 citations
TL;DR: In this paper , a modified iterative algorithm with an interpretable constraint on the optical transfer function (OTF) was employed to solve the inverse problem of noninvasive scattering imaging.
Abstract: We experimentally investigate image reconstruction through a scattering medium under white-light illumination. To solve the inverse problem of noninvasive scattering imaging, a modified iterative algorithm is employed with an interpretable constraint on the optical transfer function (OTF). As a result, a sparse and real object can be retrieved whether it is illuminated with a narrowband or broadband light. Compared with the well-known speckle correlation technique (SCT), the proposed method requires no restrictions on the speckle autocorrelation and shows a potential advantage in scattering imaging.
7 citations
TL;DR: This method can manipulate all degrees of freedom in a Jones matrix and reduce design complexity and may find applications to extend the scope of meta-optics.
Abstract: Super cells or multi-layer metasurfaces are used to realize various multi-functional and exotic functional devices. In such methods, the design space expands exponentially as more variable parameters are introduced; however, this will necessitate huge computational effort without special treatment. The function of a metasurface can be described mathematically by using a Jones matrix. When the gap between adjacent atoms is sufficiently large, the overall Jones matrix of a 3D lattice which is composed of multiple meta-atoms can be obtained by adding or multiplying each meta-atom's Jones matrix for a parallel or cascaded arrangement, respectively. Reversely, an arbitrary Jones matrix can be decomposed to achieve a combination of diagonal and rotation matrices. This means that the devices with various functions can be constructed by combining, cascading, and rotating a kind of atom, and thus the computation requirements will be reduced significantly. In this work, the feasibility of this approach is demonstrated with two cases, circular polarization selective transmission and resemble optical activity. Both the simulation and experiment are consistent with the hypothesis. This method can manipulate all degrees of freedom in a Jones matrix and reduce design complexity and may find applications to extend the scope of meta-optics.
2 citations
TL;DR: In this paper , an optical random phase DropConnect is implemented on an optical weight to manipulate a jillion of optical connections in the form of massively parallel sub-networks, in which a micro-phase assumed as an essential ingredient is drilled into Bernoulli holes to enable training convergence, and malposed deflections of the geometrical phase ray are reformulated constantly in epochs, allowing for enhancement of statistical inference.
Abstract: The formulation and training of unitary neural networks is the basis of an active modulation diffractive deep neural network. In this Letter, an optical random phase DropConnect is implemented on an optical weight to manipulate a jillion of optical connections in the form of massively parallel sub-networks, in which a micro-phase assumed as an essential ingredient is drilled into Bernoulli holes to enable training convergence, and malposed deflections of the geometrical phase ray are reformulated constantly in epochs, allowing for enhancement of statistical inference. Optically, the random micro-phase-shift acts like a random phase sparse griddle with respect to values and positions, and is operated in the optical path of a projective imaging system. We investigate the performance of the full-drilling and part-drilling phenomena. In general, random micro-phase-shift part-drilling outperforms its full-drilling counterpart both in the training and inference since there are more possible recombinations of geometrical ray deflections induced by random phase DropConnect.
1 citations
TL;DR: Zhang et al. as discussed by the authors improved the axial resolution by using the light field reconstruction with backprojection (LFBP) approach and depth estimation algorithm without loss of spatial resolution.
1 citations
DOI•
TL;DR: Guoqing Ma (马国庆), Changhe Zhou (周常河), Yongfang Xie (谢永芳), Ge Jin (金 戈), Rongwei Zhu (朱镕威), Jin Zhang (张 瑾), Junjie Yu (余俊杰), and Guohai Situ (司徒国海) as discussed by the authors
Abstract: Guoqing Ma (马国庆), Changhe Zhou (周常河), Yongfang Xie (谢永芳), Ge Jin (金 戈), Rongwei Zhu (朱镕威), Jin Zhang (张 瑾), Junjie Yu (余俊杰), and Guohai Situ (司徒国海) 1 Laboratory of Information Optics and Optoelectronic Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China 2 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China 3 Institute of Photonics Technology, Jinan University, Guangzhou 510000, China
1 citations
TL;DR: In this paper , an optical random phase DropConnect is implemented on an optical weight to manipulate a jillion of optical connections in the form of massively parallel sub-networks, in which a micro-phase assumed as an essential ingredient is drilled into Bernoulli holes to enable training convergence, and malposed deflections of the geometrical phase ray are reformulated constantly in epochs, allowing for enhancement of statistical inference.
Abstract: The formulation and training of unitary neural networks is the basis of an active modulation diffractive deep neural network. In this Letter, an optical random phase DropConnect is implemented on an optical weight to manipulate a jillion of optical connections in the form of massively parallel sub-networks, in which a micro-phase assumed as an essential ingredient is drilled into Bernoulli holes to enable training convergence, and malposed deflections of the geometrical phase ray are reformulated constantly in epochs, allowing for enhancement of statistical inference. Optically, the random micro-phase-shift acts like a random phase sparse griddle with respect to values and positions, and is operated in the optical path of a projective imaging system. We investigate the performance of the full-drilling and part-drilling phenomena. In general, random micro-phase-shift part-drilling outperforms its full-drilling counterpart both in the training and inference since there are more possible recombinations of geometrical ray deflections induced by random phase DropConnect.
01 Jan 2022
TL;DR: Li et al. as discussed by the authors implemented image prior and lightweight convolution in PhysenNet for phase retrieval and showed that better image can be reconstructed while significantly reducing the computation expenses and training time.
Abstract: We implement image prior and lightweight convolution in our PhysenNet for phase retrieval. Our results show that better image can be reconstructed while significantly reducing the computation expenses and training time.
TL;DR: In this paper , the authors provide an extensive review of practical applications of FCT in state-of-the-art combustion diagnostics and research, and the basic concepts and mathematical theory of the FCT are elaborated.
Abstract: Combustion diagnostics play an essential role in energy engineering, transportation, and aerospace industries, which has great potential in combustion efficiency improvement and polluting emission control. The three-dimensional (3D) visualization of the combustion field and the measurement of key physical parameters such as temperature, species concentration, and velocity during the combustion process are important topics in the field of combustion diagnostics. Benefiting from the non-contact and non-intrusive advantages of the optical detection method as well as the advantages of the 3D full-field measurement of the measured field by computational tomography, flame chemiluminescence tomography (FCT) has the ability to realize non-intrusive and instantaneous 3D quantitative measurement and 3D full-field visualization of key physical parameters in the combustion process, which has crucial research significance in combustion diagnostics. In this study, we review the progress of FCT technique. First, we provide an extensive review of practical applications of FCT in state-of-the-art combustion diagnostics and research. Then, the basic concepts and mathematical theory of FCT are elaborated. Finally, we introduce the conventional reconstruction algorithm and proceed to more popular artificial intelligence-based algorithms.