Journal of The Optical Society of America A-optics Image Science and Vision最新文献

Defocus is a major optical aberration affecting imaging and beam propagation across various fields. While existing measurement techniques often require complex wavefront reconstruction, modal wavefront sensors (MWSs) and astigmatic defocus sensors (ADSs) offer simpler, intensity-based methods but suffer from limited dynamic range. This study proposes a novel defocus sensing technique that combines MWS and ADS principles, using intensity measurements at just two defocus planes. The method enables fast, straightforward implementation with a significantly enhanced dynamic range. Theoretical analysis, supported by simulations, demonstrates its advantages over conventional sensors. An implementation strategy using computer-generated holography is also presented, with experimental and simulation results aligning well with theory.

This publisher's note corrects the author affiliation in the article J. Opt. Soc. Am. A42, 885 (2025)JOAOD60740-323210.1364/JOSAA.559284.

In order to solve the problem of energy loss at the center of the Cassegrain antenna, an optical system based on an elliptical-hyperbolic lens set is innovatively designed in this paper, which can shape and convert the elliptical divergent beam emitted from a semiconductor laser into a collimated hollow beam. The system consists of a lens set for collimating and shaping (C-S lens set) the laser beam and a lens set for the generation of a collimated hollow (C-H lens set) beam: the C-S lens set is responsible for shaping the elliptically divergent beam into a collimated circular beam, and the C-H lens set is used to generate a collimated hollow beam. The simulation results show that when the output beam diameter is 17.33 mm, the C-S lens set reduces the ratio of beam waist (RBW) from 1.554 to 1.014, and the standard deviation of the edge beam radius samples (SDRS) decreases from 4.227 to 0.0866 mm. The transmission efficiency of the optical system is 92.28% at 1550 nm, and the broad-spectrum performance is maintained after taking into account the practical loss factors.

The Scheimpflug principle specifies the relationship between the object, lens, and image planes, providing conditions for focusing the entire image plane even for tilted objects. This classical principle is confined to unidirectional tilting and has not been extended to camera systems with two-axis tilts. Herein, we present an extension of the Scheimpflug principle to three dimensions, demonstrating that images can be decomposed and focused independently in the vertical and horizontal directions. This simplifies focusing on the overall image, which requires a two-dimensional search, into a combination of one-dimensional searches. The proposed theory was validated through practical experimentation using cameras.

Underwater wireless optical communication (UWOC) systems face significant performance degradation due to turbulence-induced fading and pointing errors (PEs). This paper evaluates the bit error rate (BER) performance of spatial diversity UWOC systems with equal-gain combining (EGC) over independent exponentiated Weibull (EW) turbulence channels with nonzero-boresight PEs. A Parseval's theorem-based approach is used to derive an analytical expression for the average BER of EGC UWOC systems, using the Fourier transform of the conditional error probability and the characteristic function (CHF) of independent individual fading channel coefficients. Combining EW turbulence and nonzero-boresight PEs, the probability density function and CHF of the fading channel coefficient are derived, and the corresponding frequency-domain integral is approximated using a Gauss-Chebyshev quadrature formula. Taking quadrature amplitude modulation scheme as an example, the numerical results are presented and verified using Monte Carlo simulations. It is demonstrated that EGC-based spatial diversity significantly improves system robustness against the turbulence-induced fading and PEs compared to single-input single-output systems, particularly under strong fading. For instance, a diversity order of eight offers diversity gains of 37.1, 25.2, and 17.5 dB at the BER of 10^-3 when scintillation indexes are 2.5983, 0.7335, and 0.2551, respectively. The study is helpful to evaluate the BER performance of the EGC diversity UWOC system over complex fading channels.

Digital holographic tomography (DHT) is an advanced phase-imaging-based measurement technique widely used for 3D reconstruction. However, the generated phase images often suffer from significant noise interference and irregular distortions, posing challenges for accurate reconstruction. Phase unwrapping, a critical preprocessing step for 3D reconstruction in holographic tomography, is essential to correct phase discontinuities. Traditional phase unwrapping methods frequently lack the robustness and reliability required for practical applications. To address these limitations, we explore deep learning approaches and identify that existing frameworks predominantly rely on single-stage methods, which suffer from inadequate multi-scale feature fusion and a lack of phase continuity constraints, hindering high-precision cross-scale phase unwrapping. To overcome these challenges, we propose MSSPUNet, a multi-scale, multi-stage transformer network, which leverages latent features across different scales to enhance cross-scale feature fusion. This approach achieves synergistic optimization in noise suppression, phase jump correction, and detail preservation. The network was trained on extensive simulated datasets and benchmarked against several existing phase unwrapping methods. Furthermore, we validated its performance using real DHT images of cells, organoids, phantoms, and conventional 3D-printed structures. Experimental results demonstrate that MSSPUNet offers superior accuracy, enhanced robustness, and stronger generalization capabilities compared to existing methods.

This study presents and validates a systematic design framework for developing high-performance, low-magnification, compact optical systems composed of commercial off-the-shelf (COTS) components, specifically tailored for industrial applications under stringent spatial constraints. The efficacy of this methodology is demonstrated through the development of a machine vision system for the precision alignment of semiconductor probe cards. The design process is initiated by selecting the Cooke triplet as the foundational architecture, a fundamental anastigmatic structure capable of simultaneously correcting all primary aberrations. Subsequently, through a simulation-driven iterative optimization process, supplementary COTS lenses are strategically incorporated to achieve demanding performance specifications while adhering to a compact total track length of less than 50 mm. A rigorous tolerance analysis was performed to ensure the manufacturability of the design and to forecast the production yield. Following the finalization of the optical design, the corresponding optomechanical components were developed, and a prototype was assembled. The performance of the prototype was experimentally validated using a series of metrological standards, including a microscope stage micrometer, a USAF 1951 resolution test chart, and a grid distortion target. The empirical results exhibit a high degree of correlation with the predictions from a comprehensive system model that incorporates sensor effects, thereby validating the efficacy and predictive fidelity of the proposed framework. This methodology offers a robust and efficient pathway for creating high-performance, cost-effective optical solutions within spatially constrained industrial environments.

Free-space optical communications are susceptible to atmospheric turbulence. Conducting high-temporal-resolution atmospheric turbulence prediction can provide strategic support for the optimization and improvement of free-space optical communication systems, especially satellite-to-ground laser communication systems. This paper presents a new, to our knowledge, artificial neural network which is known as the Kolmogorov-Arnold network (KAN), for the prediction of atmospheric turbulence with a time resolution of 1 s using meteorological parameters. Based on the Kolmogorov-Arnold theorem, KAN establishes a nonlinear mapping relationship between meteorological parameters and the atmospheric turbulence strength. Compared with traditional neural networks, KAN is more effective at capturing complex correlations among meteorological parameters by introducing learnable nonlinear basis functions. Under the same meteorological conditions, when using the KANs to predict the atmospheric refractive-index structure constant (Cn2) at a second-level time scale, the resulting mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) are 31.54% and 31.44%, respectively. These two metrics are reduced by 6.74% and 4.35% compared to the traditional multi-layer perceptron (MLP) architecture.

The behavior of wave signals in the far zone is not only of theoretical interest but also of paramount practical importance in communications and other fields of applications of optical, electromagnetic, or acoustic waves. A long time ago, T. T. Wu [J. Appl. Phys.57, 2370 (1985)JAPIAU0021-897910.1063/1.335465] introduced models of "electromagnetic missiles" whose decay could be made arbitrarily slower than the usual inverse distance by an appropriate choice of the high-frequency portion of the source spectrum. Very recent work by Plachenov and Kiselev [Diff. Eqs.60, 1634 (2024)10.1134/S001226612460250X] introduced a finite-energy scalar wave solution, different from Wu's, decaying slower than inversely proportional with the distance. A physical explanation for the unusual asymptotic behavior of the latter will be given in this paper. Furthermore, two additional examples of scalar wave pulses characterized by abnormal slow decay in the far zone will be given, and their asymptotic behavior will be discussed. A proof of feasibility of acoustic and electromagnetic fields with the abnormal asymptotics will be described.

Beam moments of the laser beam at the receiver plane were analyzed using our previously developed formula for the average light intensity of a higher-order annular Gaussian (HOAG) beam in the presence of biological tissue turbulence. HOAG beam moments are examined for the entities of power-in-the-bucket (PIB) and kurtosis across various tissue types such as the upper dermis (human), liver parenchyma (mouse), intestinal epithelium (mouse), and deep dermis (mouse). Moreover, beam moments are explored considering factors like the strength coefficient of the refractive-index fluctuations and the propagation distance. The PIB values for all HOAG beam modes are found to decrease exponentially and steadily, behaving similar to Gaussian beams as tissue length increases. As turbulence intensity increases, higher-order HOAG beam modes transfer optical energy to the receiver more efficiently than the lower order modes. Kurtosis analysis shows that at intermediate distances, the beam energy is distributed toward the edges, while at longer distances, the energy concentration is lower at the edges than at the center. This trend is reflected in increasing kurtosis values across all HOAG modes and tissue types. Considering the changes in PIB and kurtosis, higher-order HOAG modes transfer energy more conservatively within the tissue. Furthermore, the tissue type with the best transfer of optical power was observed to be the deep dermis (mouse).