Article
Multidisciplinary Sciences
Hyeonseung Yu, Youngrok Kim, Daeho Yang, Wontaek Seo, Yunhee Kim, Jong-Young Hong, Hoon Song, Geeyoung Sung, Younghun Sung, Sung-Wook Min, Hong-Seok Lee
Summary: Recent research has shown that holographic displays can generate photorealistic 3D holograms in real time. However, obtaining high-quality real-world holograms has been a challenge, limiting the development of holographic streaming systems. In this work, the authors develop a deep learning-based incoherent holographic camera system that can deliver visually enhanced holograms in real time. By applying a neural network to filter noise in captured holograms and integrating a holographic camera and display, they demonstrate a holographic streaming system.
NATURE COMMUNICATIONS
(2023)
Article
Optics
Zhenzhong Lu, Yuping Cao, Min Liu, Biao Han, Jiali Liao, Yanling Sun, Lin Ma
Summary: Based on the generative-discriminative model, a digital holographic reconstruction generative adversarial network (DHR-GAN) is established to evaluate perceptual holographic images. An experimental acquisition system is designed to prepare the digital holographic reconstructed image dataset (DHR-dataset) for supervised training and testing of DHR-GAN. Extensive qualitative and quantitative comparisons demonstrate the effectiveness of the proposed network model in achieving more stable reconstruction results with higher contrast and sharper local details.
OPTICS AND LASER TECHNOLOGY
(2023)
Article
Optics
Shuo Wang, Xianan Jiang, Xu Liu, Zhao Dong, Ruijing Pei, Huaying Wang
Summary: This article introduces a phase recovery method based on deep learning, which enables the phase and amplitude information of multiple holograms with different out-of-focus distances to be reconstructed quickly. The success of this deep learning multi-scale hologram self-focusing reconstruction method is demonstrated by self-focusing the holographic microscopic images of human red blood cells and chicken blood cells. Compared with existing methods, this method improves the quality of the reconstructed image and the reconstruction speed. Compared with U-NET, the peak signal-to-noise ratio (PSNR) and structural similarity of the reconstructed network output are increased, which can suppress the influence of speckle noise well.
OPTICS COMMUNICATIONS
(2023)
Article
Energy & Fuels
Yuqi Wu, Senyou An, Pejman Tahmasebi, Keyu Liu, Chengyan Lin, Serveh Kamrava, Chang Liu, Chenyang Yu, Tao Zhang, Shuyu Sun, Samuel Krevor, Vahid Niasar
Summary: Digital rock physics (DRP) is an effective tool for predicting petrophysical properties and mass transport mechanisms in porous media. However, acquiring 3D high-resolution and large-view images for accurate prediction is challenging. To address this, we used FastGAN to synthesize LR and HR images and then applied CycleGAN to reconstruct 3D HR digital rocks. The accuracy of our proposed method (FastGAN-CycleGAN) was validated by comparing with laboratory measurements.
Review
Optics
Hai Yu
Summary: This paper introduces an angular displacement measurement technology based on in-line digital holographic reconstruction, achieving high measurement resolution by reconstructing the optical information of calibrated gratings in a small volume.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Optics
Anthony Berdeu, Thomas Olivier, Fabien Momey, Loic Denis, Frederic Pinston, Nicolas Faure, Corinne Fournier
Summary: In-line digital holography is a powerful tool for imaging absorbing and phase objects, but the holograms can be corrupted by background signals. By using two holograms of the same object at different locations, an inverse problems approach can be applied to estimate the sample's complex transmittance and the contribution of background interference. Experimental results with stained bacteria demonstrate improved reconstructions of the sample while considering the background contribution.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Biophysics
Youngdo Kim, Jihwan Kim, Eunseok Seo, Sang Joon Lee
Summary: In this study, a novel image-based technique using digital in-line holographic microscopy (DIHM) and artificial intelligence (AI) was proposed to measure the 3D position and orientation of normal red blood cells (RBCs). By combining deep learning algorithms and digital image processing techniques, the 3D positional, orientational, and morphological information of each RBC can be obtained within a short time. This method could be further applied to analyze the dynamic translational and rotational motions of abnormal RBCs in shear flows in hematologic disorders.
BIOSENSORS & BIOELECTRONICS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Nikita Moriakov, Jan-Jakob Sonke, Jonas Teuwen
Summary: The paper proposes LIRE, a learned invertible primal-dual iterative scheme for CBCT reconstruction, to address the limitations of current methods. LIRE combines U-Net and residual CNN architectures to reduce memory requirements while maintaining expressive power. The method outperforms classical methods and deep learning baselines in terms of reconstruction quality and generalization on test sets. Additionally, LIRE can be finetuned to reconstruct high-resolution CBCT data with improved performance.
Article
Optics
Xianfeng Xu, Weilong Luo, Hao Wang, Xinwei Wang
Summary: A method using deep learning network for image reconstruction with a single hologram in digital holography is proposed. The method enhances the space bandwidth product, reduces the storage loads, and is immune to most disturbances.
Article
Engineering, Electrical & Electronic
Sanjeev Kumar, Manjunatha Mahadevappa, Pranab Kumar Dutta
Summary: The article presents a lensless microscopy technique using a spatially extended white LED with low spatial and very low temporal coherence as the light source. By decomposing the convolution operation, the number of unknown parameters to be estimated is drastically reduced, leading to an improvement in resolution.
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
(2021)
Article
Chemistry, Multidisciplinary
Bo Chen, Zhaoyi Li, Yilin Zhou, Yirui Zhang, Jingjing Jia, Ying Wang
Summary: In this paper, an improved deep-learning model Mimo-Net is proposed to address the simultaneous reconstruction of intensity and phase information in multiscale digital holography. Local feature extraction is performed to generate holograms of different scales, branch input training is used to realize multiscale feature learning, and feature information of different receptive fields is obtained. The up-sampling path outputs multiscale intensity and phase information simultaneously through dual channels. Experimental results show that Mimo-Net can perform intensity and phase reconstruction simultaneously on three different scales of holograms with improved reconstruction efficiency compared to Y-Net.
APPLIED SCIENCES-BASEL
(2023)
Article
Optics
Chen Wang, Weikang Wang, Jiasi Wei, Junjie Wu, Xiangchao Zhang, Huadong Zheng, Famin Wang, Yingjie Yu
Summary: Digital holography has transformative potential in measuring stacked-chip microstructures due to its noninvasive, single-shot, full-field characteristics. However, uncertainties in reconstruction distance inevitably lead to resolving blur and reconstruction distortion. In this study, a phase-based reconstruction optimization method was proposed, which involved setting a reconstruction distance range, obtaining phase information using sliced numerical reconstruction, and optimizing the reconstruction distance to identify the focal plane of the reconstructed image. The effectiveness of the proposed method was verified through simulations and experiments, showing significant improvement in step-height characterization accuracy.
Article
Optics
Ali Akbar Khorshad, Nicholas Devaney
Summary: This article develops an in-line digital holographic microscopy (DHM) based on a gradient-index (GRIN) rod lens and compares it with a conventional pinhole-based DHM. The results show that the GRIN-based setup provides better resolution in a high-magnification regime.
Article
Engineering, Civil
Luca Anzalone, Paola Barra, Silvio Barra, Aniello Castiglione, Michele Nappi
Summary: This work combines Curriculum Learning with Deep Reinforcement Learning to learn a competitive driving policy without prior domain knowledge in the CARLA autonomous driving simulator. The approach divides the reinforcement learning phase into multiple stages of increasing difficulty, guiding the agent towards an increasingly better driving policy. The agent architecture includes various neural networks and novel value decomposition scheme and gradient size normalization function. Quantitative and qualitative results of the learned driving policy are presented.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Jinxiang Song, Christian Haeger, Jochen Schroeder, Alexandre Graell Amat, Henk Wymeersch
Summary: We propose an autoencoder-based transceiver for a wavelength division multiplexing system impaired by hardware imperfections. The autoencoder is designed following the architecture of conventional communication systems, enabling it to have similar performance and improve training convergence rate. Simulation results show that the proposed autoencoder significantly outperforms the conventional approach in terms of spectral efficiency.
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
(2022)
Article
Optics
Joseph Rosen, Hilton B. de Aguiar, Vijayakumar Anand, YoonSeok Baek, Sylvain Gigan, Ryoichi Horisaki, Herve Hugonnet, Saulius Juodkazis, KyeoReh Lee, Haowen Liang, Yikun Liu, Stephan Ludwig, Wolfgang Osten, YongKeun Park, Giancarlo Pedrini, Tushar Sarkar, Johannes Schindler, Alok Kumar Singh, Rakesh Kumar Singh, Guohai Situ, Mitsuo Takeda, Xiangsheng Xie, Wanqin Yang, Jianying Zhou
Summary: In recent years, the rapid development of chaos-inspired imaging technologies, consisting of non-invasive and invasive directions, has led to faster and smarter imaging capabilities. Non-invasive imaging through scattering layers has achieved significant progress, while invasive imaging exploits chaos to achieve special imaging characteristics and increase dimensionalities beyond the limits of conventional imagers. This roadmap presents the current and future challenges in both invasive and non-invasive imaging technologies.
APPLIED PHYSICS B-LASERS AND OPTICS
(2022)
Article
Physics, Applied
Xiangyu Zhang, Fei Wang, Guohai Situ
Summary: The solution of an inverse problem in computational imaging often relies on accurate knowledge of the physical model and object, but model uncertainty in practical applications can degrade the quality of reconstructed images. In this paper, we propose a novel untrained learning approach to address computational imaging with model uncertainty, demonstrated through phase retrieval, an important task in biomedical imaging and industrial inspection.
JOURNAL OF PHYSICS D-APPLIED PHYSICS
(2022)
Article
Optics
Fei Wang, Chenglong Wang, Mingliang Chen, Wenlin Gong, Yu Zhang, Shensheng Han, Guohai Situ
Summary: Ghost imaging (GI) is a technique that facilitates image acquisition under low-light conditions by single-pixel measurements, and has great potential in various applications. However, the current GI techniques require a large number of samples to reconstruct high-resolution images, which limits their practical applications. In this study, a far-field super-resolution GI technique is proposed that incorporates the physical model for GI image formation into a deep neural network. The proposed technique outperforms other widespread GI techniques in terms of spatial resolution and sampling ratio, and provides a new framework for GI with practical applications.
LIGHT-SCIENCE & APPLICATIONS
(2022)
Article
Optics
Fei Wang, Chenglong Wang, Chenjin Deng, Shensheng Han, Guohai Situ
Summary: This study proposes a physics-enhanced deep learning approach for image reconstruction in single-pixel imaging. By combining a physics-informed layer and a model-driven fine-tuning process, the proposed method demonstrates generalizability and outperforms other widespread algorithms in terms of both robustness and fidelity.
PHOTONICS RESEARCH
(2022)
Article
Optics
Dong Liang, Xiaoting Peng, Yuyao Hu, Fu Zhao, Shanshan Zheng, Guohai Situ, Jun Liu
Summary: Light sheet fluorescence microscopy (LSFM) using light field imaging can improve axial resolution and provide high axial resolution and large field of view without losing spatial resolution.
OPTICS AND LASERS IN ENGINEERING
(2022)
Article
Optics
Dajiang Lu, Qi Xing, Meihua Liao, Guohai Situ, Xiang Peng, Wenqi He
Summary: In this study, we experimentally investigate image reconstruction through a scattering medium under white-light illumination. A modified iterative algorithm with a constraint on the optical transfer function (OTF) is used to solve the inverse problem of noninvasive scattering imaging. The proposed method shows a potential advantage in scattering imaging compared to the well-known speckle correlation technique (SCT).
Article
Optics
Yong-Liang Xiao, Sikun Li, Guohai Situ, Jianxin Zhong
Summary: This Letter presents a unitary neural network based on optical random phase DropConnect, which introduces a micro-phase to achieve training convergence and enhance statistical inference. The study reveals that the partial drilling of random micro-phase-shift outperforms the full-drilling counterpart in both training and inference.
Article
Nanoscience & Nanotechnology
Xiangyu Zhang, Chenjin Deng, Chenglong Wang, Fei Wang, Guohai Situ
Summary: Single-pixel imaging (SPI) converts a multi-dimensional image acquisition problem into a one-dimensional temporal-signal detection problem, and efficient SPI techniques are crucial for image reconstruction. Deep learning has shown superiority in SPI, but is task-specific and requires retraining for different problems. The proposed VGenNet algorithm incorporates a model-driven fine-tuning process into a generative model, allowing the use of a pretrained model for various inverse imaging problems. Experimental results demonstrate the high-quality image reconstruction and flexibility of VGenNet.
Article
Optics
Hao Wang, Jiabei Zhu, Jangwoon Sung, Guorong Hu, Joseph Greene, Yunzhe LI, Seungbeom Park, Wookrae Kim, Myungjun Lee, Yusin Yang, Lei Tian
Summary: Topography measurement is crucial for surface characterization and inspection applications. This study presents a novel topography technique called Fourier ptychographic topography (FPT), which combines a computational microscope and a phase retrieval algorithm to achieve wide-field-of-view and high-resolution topography reconstruction with nanoscale accuracy. FPT has important implications for surface characterization, semiconductor metrology, and inspection applications.
Article
Nanoscience & Nanotechnology
Jinming Gao, Jinying Guo, Anli Dai, Guohai Situ
Summary: Imaging in scattering media has always been a major challenge due to the interference of background noise with the object information carried by ballistic light, resulting in poor image quality. To address this, active illumination imaging technology offers various advantages over passive imaging by introducing controllable parameters like polarization and coded aperture. In this study, we successfully introduced orbital angular momentum into scattering imaging, effectively enhancing the mid/high frequency components of the object. Furthermore, we fused the enhanced image with the low-quality image obtained from traditional imaging, leading to significant improvements in signal-to-noise ratio and image contrast. This method holds potential applications in foggy environments for autonomous driving, lidar, and machine vision.
Article
Optics
Jianying Hao, Xiao Lin, Yongkun Lin, Mingyong Chen, Ruixian Chen, Guohai Situ, Hideyoshi Horimai, Xiaodi Tan
Summary: In this study, a complex amplitude demodulation method based on deep learning was proposed for holographic data storage. A single-shot diffraction intensity image was used to demodulate both the amplitude and phase by analyzing the correlation between the diffraction intensity features and the encoding data pages. This method achieved multilevel complex amplitude demodulation experimentally without iterations for the first time in HDS.
OPTO-ELECTRONIC ADVANCES
(2023)
Article
Chemistry, Multidisciplinary
Guocui Wang, Jinying Guo, Xinke Wang, Bin Hu, Guohai Situ, Yan Zhang
Summary: This article introduces the methods of using super cells or multi-layer metasurfaces to achieve various multi-functional and exotic functional devices. By mathematically describing the Jones matrix, the computational requirements can be reduced, and devices with various functions can be constructed.
Article
Astronomy & Astrophysics
Situ GuoHai
Summary: Scattering of coherent light and coherent vortex beams propagating through a segment of a multi-mode fiber both produce speckle. Recognizing the orbital angular momentum of the incident vortex beam from the output speckle becomes significantly important for vortex beam study and mode division multiplexing, and deep learning is demonstrated as an efficient approach to address this problem.
SCIENTIA SINICA-PHYSICA MECHANICA & ASTRONOMICA
(2022)
Article
Optics
Zhu Sunyong, Jin Ying, Wu Quanying, Liu Haishan, Situ Guohai
Summary: A hybrid neural network model based on 3D-2D convolution tandem is proposed to overcome the problem of low accuracy in practical flame reconstruction. The model uses 3D convolution to extract spatial features from multiple views and 2D convolution to accelerate training speed. Compared to traditional algorithms, the proposed model achieves higher accuracy with lower time consumption.
ACTA OPTICA SINICA
(2022)
Article
Optics
Junfeng Hou, Guohai Situ
Summary: Optical technologies have been widely used in information security, but the linearity issue in current optical encryption techniques makes the system vulnerable to attacks. Recent research shows that a true nonlinear optical encryption technique can be achieved by utilizing the self-phase modulation effect of photorefractive crystals, providing robustness against known plaintext attacks.
