Optical Review最新文献

Hyperspectral (HS) imaging, which acquires the detailed spectral information of an object, has attracted extensive interest in various fields, such as remote sensing, agriculture, and biomedicine. In the dawn of HS imaging, HS imaging relied primarily on time-consuming scanning techniques to achieve high spatial and spectral resolution, limiting its applicability. Subsequently, snapshot (non-scanning) HS imaging emerged, enabling high-speed capturing. The snapshot method captures HS images in a single exposure, but it has the drawback of lower spatial resolution. With recent advancements in computational technology, high-efficiency HS imaging has been pursued in cooperation with image post-processing. In this review, the historical evolution of HS imaging is described focusing on spectroscopic techniques. An up-to-date HS imaging technique, computational HS imaging using a spatial-spectral random coded mask, is introduced with a brief explanation of its working principle. The HS imaging technique demonstrates outstanding characteristics in terms of sensitivity, spatial resolution, spectral accuracy, and frame rate. Several applications of HS imaging are introduced showing improved accuracy of image analysis compared to traditional RGB image analysis. Finally, the remaining challenges and prospects are discussed.

Underwater Optical Wireless Communication (UOWC) is a key enabler of the Internet of Underwater Things (IoUT), offering high-speed and low-latency data transmission in aquatic environments. However, system performance is highly sensitive to environmental factors such as absorption, scattering, turbulence, and noise. This study presents a comprehensive, simulation-based performance analysis and optimization of UOWC systems using Opti-System software, focusing on laser diodes operating at 450 nm and 520 nm. Four representative water types—pure seawater, clear ocean, coastal ocean, and turbid harbor—are examined to evaluate key performance metrics, including received power, Q-factor, eye diagrams, signal-to-noise ratio (SNR), bit error rate (BER), and transmission range. An environment-aware optimization framework is proposed, correlating wavelength selection and system configurations with the optical properties of each water type. The impact of transmitter power, beam divergence, photodetector sensitivity, and modulation (OOK) is assessed to identify optimal design choices for robust communication. Simulation results reveal that shorter wavelengths perform better in clearer waters, while adaptive tuning strategies significantly enhance reliability in highly scattering or turbulent conditions. This work provides practical design insights and optimization strategies to support scalable, efficient, and resilient UOWC systems for real-world IoUT applications.

To compensate for time-fluctuating spatial noise and reconstruct an object image, a deep learning-based single-pixel imaging (SPI) system using a neural network consisting of five transposed convolutional layers and three convolutional layers has been developed. In the present study, we proposed a new image reconstruction method using deep learning with a feature-extended time-division pattern-learning (TDPL) network, which further increased the number of features in each layer to enhance the tolerance to time-fluctuating spatial noise. Simulations and experiments were performed to compare the performance of the proposed network with that of conventional methods, such as computational ghost imaging, Hadamard single-pixel imaging, deep convolutional auto-encoder network (DCAN), and TDPL network. We found that the image quality of the reconstructed image using the proposed method is superior to that of conventional methods in any environment with time-fluctuating spatial noise. For example, the quality of an object image reconstructed using the proposed method improved by − 0.037 and − 0.014 in a root-mean-square error and + 0.083 and + 0.005 in a structural similarity compared to that using the DCAN and TDPL network, respectively, under time-fluctuating spatial noise with a standard deviation of 0.5. Therefore, the proposed deep learning-based SPI system with a feature-extended TDPL network is expected to be applied to various imaging or observation in an environment where conditions are likely to change, such as astronomical observations, remote monitoring, and optical wireless communications.

We present a rapid wavefront sensor based on machine learning and using a line-scan camera. The object light wave propagates through a scattering medium. In our method, the scattered light wave undergoes a series of preconditioning steps. The resultant light wave, in which only the wavefront aberration component is emphasized and the reference object light wave is removed, is captured as one-dimensional data using line focusing optics. The captured data are trained by a convolutional neural network, and the trained network can estimate the Zernike coefficients without iterative calculations. The proposed method achieves significantly faster measurement compared to a two-dimensional sensor. The proposed method was experimentally demonstrated, as a proof of concept, using a line-scan camera and a preconditioning method that we designed.

Head-mounted displays (HMDs) for augmented reality (AR) require optical systems that deliver high luminance, high optical see-through performance, and high image resolution. Conventional two-directional pupil expansion using beam splitter array (BSA) waveguides often suffers from low light coupling efficiency and degraded image quality due to narrow coupling mirrors and wavefront disturbances caused by exposed mirror edges along the total internal reflection surface. We propose a novel HMD optical system that combines a pupil expansion (PE) prism with a one-directional BSA waveguide. The PE prism features an enlarged coupling surface, efficiently capturing light from the light engine. Importantly, the PE prism is designed to avoid exposed mirror edges in the optical path, thereby eliminating wavefront disturbances and enabling high-resolution virtual image projection. Within the PE prism, multiple ray paths are generated through complex reflections and transmissions via overlapping multi-mirrors. This multi-path propagation not only expands the eye-box orthogonally to the BSA waveguide, but also ensures uniform output luminance. A prototype HMD implementing the proposed method achieves high luminance (over 5000 cd/m²), high image resolution (MTF > 58% at 10 cycles/degree), high optical see-through transparency (over 94%), and luminance uniformity (over 53%). These results demonstrate significant improvements over conventional BSA waveguide approaches.

The exact analytical solution of the electric field of the Airy beam with a polarization in the x-direction is derived by the vector angular spectrum theory. And then, the evanescent interference of two symmetrically offset evanescent Airy beams is investigated by the angular spectrum theory in detail. Two separated Airy beams at the initial position will overlap and interfere due to the lateral self-acceleration. The y-axis interference field shows a superior stability and a slower decay compared to the x-axis. The interference optical field forms a spindle-shaped central optical field along the y-axis, and the intensity rapidly decays along the x-axis, and a small-scale scattered optical field is formed in the low-intensity regions on both sides. The result is useful to elucidating the formation mechanism and theoretical properties of the interference phenomenon of Airy beams.

This study presents a theoretical and numerical investigation of local field enhancement and optical bistability in ZnSe-based core–shell nanocomposites, providing insights for photonic applications. The structures consist of a ZnSe dielectric core and a metallic shell of either silver ((textrm{Ag})) or gold ((textrm{Au})), embedded in oxide matrices ((textrm{SiO}_2), (textrm{ZnO}), or (textrm{HFO}_2)) with varying permittivities. Using the quasi-static approximation and the Lorentz–Drude model, we analyze how variations in the core radius (keeping the outer radius fixed) influence spectral response and local field enhancement. Results show that smaller cores (thicker shells) yield stronger local field enhancement factors ((textrm{LFEF})) due to increased plasmonic confinement. Ag shells produce sharper, more intense resonances than Au, attributed to lower damping and superior plasmonic performance. The dielectric environment also plays a key role: low-permittivity matrices like (textrm{SiO}_2) support higher field localization, while high-permittivity ones such as (textrm{HFO}_2) weaken confinement and blue-shift the resonances. Nonlinear analysis reveals that thicker shells and lower matrix permittivity enhance bistability and reduce switching thresholds, particularly in Ag-based systems. These findings highlight the critical influence of geometry and material choice on both linear and nonlinear optical responses. The results offer practical guidance for engineering core–shell nanocomposites tailored for applications in photonic devices, optical sensors, and all-optical switching.

In this study, we investigate the time-fractional perturbed nonlinear Schrödinger equation in the context of optical fibers using the newly extended mapping scheme. As a result, many types of traveling wave solutions are obtained, including novel solitary wave solutions, triangular, hyperbolic, and periodic wave solutions expressed in terms of Jacobi elliptic functions. Solutions are obtained as well in the limiting cases for (ell) approach 0 or 1. By assigning specific values to the free parameters, the physical significance of the 2D and 3D geometric shapes of the derived solutions is discussed, and the corresponding physical variations are illustrated. This work demonstrates the applicability of the proposed method to a broader class of nonlinear evolution equations in physics and engineering.

Recent advances in lensless imaging reconstruction have primarily relied on supervised neural models trained using target images captured by lensed cameras via a beam splitter. However, we argue that using reference images from a different optical system introduces bias into the reconstruction process. To mitigate this issue, we propose a self-supervised approach that leverages data-fidelity guidance, similar to deep image prior, to train neural models for single-iteration lensless reconstruction. Through simulations and prototype camera experiments, we demonstrate that combining simple convex optimization methods with a denoising UNet improves perceptual quality (LPIPS), accelerates inference compared to traditional optimization techniques, and reduces potential unwanted biases in the reconstruction network.

Detectors based on cross delay-line (XDL) anodes with charge induction are widely applicable in space astronomical telescopes, deep space exploration, and fluorescence lifetime measurements. In this article, a three-dimensional structure XDL anode based on Printed Circuit Board (PCB) technology is proposed, which can conveniently realize high-resolution detection with a simple process and low cost. We theoretically studied the charge induction principle and established a model of the XDL anode through the finite element method. The model allows us to determine anode parameters, such as anode strip width, inter-strip distance, and substrate thickness, to optimize the output signal on the XDL anode, thereby indirectly affecting the resolution of the detector. Based on the experimental platform, we systematically characterized the key performance parameters of the detector. We conclude that the spatial resolution of the detector is better than 50 (upmu)m and the non-linearity is less than 5(%).