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

This work proposes the use of anisotropic materials for the design of planar multilayer systems that produce polarization-dependent structural color, both by reflection and/or transmission. To obtain reflected colors, 1D perfectly periodic structures composed of anisotropic materials are used. For transmitted colors, we propose to assemble two identical photonic crystals comprising alternating layers of isotropic and anisotropic materials that differ in their definition of the unit cell. In such a system, a topological mode is generated at the interface between the two photonic crystals. These colors not only exhibit high brightness and purity, but can also be tuned by varying the layer thicknesses. Furthermore, their brightness can be controlled by changing the direction of the incident polarization. These systems are very promising for the design of materials with controllable color, for applications such as optical sensors and anticounterfeiting technologies.

We present an inverse method to compute freeform optical surfaces that transform a light distribution, parameterized by two source planes, into two separate target distributions. The surfaces can be reflectors or lenses, and control both the spatial and directional source and target coordinates of light rays. From energy conservation, we derive Jacobian equations for optical mappings, and the optical path length provides generating functions for the optical surfaces. A three-stage least-squares algorithm numerically solves the resulting equations. We present examples with complex source and target distributions.

We analyze correspondence ghost imaging (CGI) from a machine learning perspective, focusing on its intrinsic mathematical consistency with the perceptron model. Based on this analysis, linear classification algorithms such as the perceptron and logistic regression are applied to CGI data, yielding improved image quality compared to traditional methods. Experimental and simulation results demonstrate that logistic regression achieves the highest structural similarity index (SSIM) in reconstruction tasks. Furthermore, CGI is employed for solving standard classification tasks, achieving comparable accuracy with dramatically lower computation time-up to 10× faster than logistic regression. These findings uncover the potential of interpreting CGI as a lightweight linear classifier.

Wavefront estimation under long-distance, strong atmospheric turbulence remains a critical challenge in free-space optical communication (FSOC). Conventional approaches always suffer from high computational cost and latency. To address this issue, we proposed a lightweight high-precision neural network (LHP-Net), a compact yet accurate model that directly predicts Zernike coefficients from single-frame distorted images under long-distance, strong atmospheric turbulence. The architecture combines an optimized convolutional backbone with a lightweight Zernike-aware attention (LZA) module, enhancing the sensitivity to turbulence-induced aberrations while minimizing computational cost. To rigorously evaluate performance, a large-scale dataset using spectral phase screen simulations was obtained, covering propagation distances up to 10 km and turbulence intensity ranging from weak to strong. Simulation results indicate that LHP-Net achieves up to 92.4% lower prediction error and 37.5% faster inference, exhibiting better performance than a conventional convolutional neural network (CNN). Furthermore, our hybrid training strategy significantly enhances the generalization across different turbulence intensities. Remarkably, LHP-Net maintains robust performance even under extreme turbulence, exhibiting minimal prediction error, providing potential for real-time adaptive optics in next-generation free-space optical systems.

To observe the moiré phenomenon, two grids are required. Nevertheless, the effect can be achieved without a physical grid, using computer-generated grids based on a fragment of the image. This approach allows for the observation of the moiré effect without a physical grid. The computer-generated grid replicates the vibration of the object and, therefore, can be used for practical displacement measurements. The principle has been confirmed by simulations and experimental tests. The proposed method enables measurements with significantly reduced optical target sizes. This may make moiré measurements more convenient and accurate.

We propose PIRNet, a lightweight, physics-informed deep learning framework for non-interferometric wavefront sensing in optical beam expansion systems. The network simultaneously estimates spherical aberration, coma, and astigmatism from single-shot beam intensity patterns. A large-scale dataset is generated by simulating vortex beam propagation through combined aberrations using the ABCD transfer matrix method. To ensure physical plausibility, we introduce a physics-consistency loss that reconstructs the beam pattern from the predicted coefficients via an optical propagation model and compares it with the input, dynamically balanced with the regression loss through a learnable uncertainty weighting mechanism. A staged training strategy stabilizes convergence by first focusing on regression before introducing the physical constraint. Comparative experiments demonstrate that PIRNet outperforms ResNet18, ResNet34, ResNet50, and Xception across multiple metrics under varying noise levels and cropping ratios. The integration of physical priors enhances both accuracy and generalization, positioning PIRNet as a promising approach for model-driven wavefront characterization in adaptive optics and free-space optical communication.

One of the most common methods for reconstructing three-dimensional (3D) images of real or computer-generated objects is digital and computer-generated holography, respectively. Both techniques rely on the use of electro-optical devices that modify the phase or amplitude of light fields in a controlled manner, the so-called spatial light modulators. However, given that holography typically requires coherent light sources, a common problem with three-dimensional projection is the crosstalk between the layers that make up the 3D object. This limits full-depth control and directly affects image quality. Interestingly, in the past few years, several methods have proven to be effective in breaking layer crosstalk by erasing the spatial coherence of light. A drawback of such solutions is that, in many cases, additional optical resources are required to achieve such a task. In this work, we present a method for high-density reconstruction of three-dimensional objects using rapid modulation of light fields by means of digital micromirror devices (DMDs). The 3D reconstruction is performed by discretizing the object into multiplane light-point contours, where the resolution of the contours is controlled by the density of the light points. The high refresh rate of the DMD (∼10kHz) allows for a reconstruction where each point of the 3D image is spatially and temporally controlled by independent amplitude holograms, thus effectively eliminating coherence-induced multiplane crosstalk without the need for additional optical elements. Because of its simplicity and versatility, we believe that our method provides a practical route toward compact, high-resolution 3D holographic projectors.

High-dimensional quantum key distribution (HD-QKD) is limited by atmospheric turbulence, which scrambles the spatial modes used to encode information. A critical challenge is the lack of general methods to diagnose how this decoherence occurs. We apply the vector coherent mode decomposition (VCMD)-a numerical framework that represents a partially coherent beam as an incoherent superposition of its natural, orthogonal eigenmodes-as a powerful diagnostic engine for this problem. We simulate a qudit state propagating through a turbulent channel and use VCMD to quantify the decay of channel fidelity and reveal the exact spatial structure of the dominant error modes. This provides a complete modal fingerprint of the channel's decoherence mechanism. To establish the framework's credibility, we first validate its accuracy against a benchmark suite, including unmasking a deceptive "Masked Gaussian" beam and quantifying the purity of a decohered optical skyrmion. While the theory of vector coherence is established, this work consolidates it into a practical, basis-independent framework and applies it to discover the physical error modes of a turbulent quantum channel, a task for which conventional, basis-dependent methods are ill-suited.

Color quantization, reducing the number of distinct colors in a given image with minimal distortion, is a common image processing operation with many applications in visual computing. Heckbert's median cut algorithm, which dates back to the early 1980s, is generally considered the first true color quantization algorithm. Heckbert's seminal work generated numerous subsequent studies extending his algorithm in various ways. In this retrospective, we present a detailed analysis of the median cut algorithm and demonstrate how it influenced later color quantization, vector quantization, and data clustering algorithms.

A spectral element method is developed for determining the propagation constants of guided and leaky modes in planar multilayer waveguides. The originality of the technique lies in what we believe to be a novel combination of a hierarchical basis built with modified Legendre polynomials and a rational mapping that efficiently handles semi-infinite computational subdomains. To handle leaky waves, we extend the algebraic mapping to complex coordinates that act as perfectly matched layers (PMLs). The method thus naturally enforces both radiation conditions at infinity and boundary conditions at material interfaces, leading to improved convergence properties without additional constraints or numerical treatments. The efficiency and numerical precision of our method are demonstrated through comparisons with results from the literature for high-index-contrast dielectric and plasmonic waveguides, ARROW anisotropic structures, and quantum well waveguides.