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

Matrix functions are, of course, indispensable and of primary concern in polarization optics when the vector nature of light has been considered. This paper is devoted to investigating matrix-based Fourier analysis of two-dimensional matrix signals and systems. With the aid of the linearity and the superposition integral of matrix functions, the theory of linear invariant matrix systems has been constructed by virtue of six matrix-based integral transformations [i.e., matrix (direct) convolution, matrix (direct) correlation, and matrix element-wise convolution/correlation]. Properties of the matrix-based Fourier transforms have been introduced with some applications including the identity impulse matrix, matrix sampling theorem, width, bandwidth and their uncertainty relation for the matrix signal, and Haagerup's inequality for matrix normalization. The coherence time and the effective spectral width of the stochastic electromagnetic wave have been discussed as an application example to demonstrate how to apply the proposed mathematical tools in analyzing polarization-dependent Fourier optics.

The analytical propagation formulae of a single multi-Gaussian Schell model (MGSM) beam and two MGSM beams in strongly nonlocal nonlinear media (SNNM) are derived, and the optical breather characteristics of a single MGSM beam and two MGSM beams in SNNM are studied, respectively. It is found that a MGSM soliton is never formed because of the self-shaping feature of MGSM beams, but a MGSM breather can be formed. Furthermore, for a MGSM breather, the Gaussian-like profile and the flat-top profile alternate periodically during propagation. On the other hand, even if the separation distance is large enough, two MGSM breathers can be combined into a single breather due to nonlinearity when the threshold critical power arrives. Furthermore, the relationship between the threshold critical power and the MGSM beam parameters is also investigated.

The $pm 1$st order diffraction of gratings is widely used in spectral analysis. However, when the incident light is non-monochromatic, the higher-order diffractions generated by traditional diffraction gratings are always superimposed on the useful first-order diffraction, complicating subsequent spectral decoding. In this paper, single-order diffraction gratings with a sinusoidal transmittance, called hexagonal diffraction gratings (HDGs), are designed using a convolutional neural network based on deep learning algorithm. The trained convolutional neural network can accurately retrieve the structural parameters of the HDGs. Simulation and experimental results confirm that the HDGs can effectively suppress higher-order diffractions above the third order. The intensity of third-order diffraction is reduced from 20% of the first-order diffraction to less than that of the background. This higher-order diffraction suppression property of the HDGs is promising for applications in fields such as synchrotron radiation, astrophysics, and soft x-ray lasers.

The transmission dynamics of a circular Airy beam (CAB) with quadratic phase modulation (QPM) and cross-phase modulation (XPM) in the cubic-quintic nonlinear fractional Schrödinger equation (FSE) optical system is investigated. In the linear case, the energy distribution of the beam is affected by XPM and the focusing position of the beam is influenced by QPM. CAB undergoes splitting and its intensity is shifted as the absolute value of the XPM coefficient (|c|) increases. When XPM coefficients are opposite to each other, CABs are transmitted in opposite states in space. The degree of interference between beams gradually enhances with the increase of the XPM coefficient, leading to the formation of interference resembling water ripples. In the nonlinear regime, different results (evolving into solitons or undergoing diffraction transmission) are observed in CABs based on cubic-quintic nonlinear combination modes. Furthermore, nonlinear combination modes that can generate solitons and changes in solitons under actions of XPM and QPM are studied in detail. The distribution of solitons can be altered by positive or negative XPM, and solitons exist when QPM coefficients are within a certain range. The spacing and number of solitons can be modified by adjusting the magnitude of the QPM coefficient. The research shows that the control for solitons (number, distribution, and propagation) can be achieved through flexible selection of cubic-quintic nonlinear combination modes and parameter optimization (XPM coefficient, QPM coefficient, Lévy index).

Functional neuroimaging with widefield optical imaging (WOI) is potentially useful for studying developmental disorders in juvenile mice. However, WOI requires an intact-skull cranial window, and the effects of such windows on young mice are unknown. We performed intact-skull cranial window placement on mice as young as P7 to study the effects of chronic placement. Cranial windows placed at young ages (P7 and P10) were not longitudinally stable, resulting in significant attrition. Windows placed at ages P14 or less resulted in significant impairment to skull growth, which in turn caused artifacts in resting-state functional connectivity analysis. Longitudinal cranial windows should likely be avoided under P30.

The Lie algebraic approach provides a systematic method to determine aberration coefficients for ray propagation in inhomogeneous media. We present the underlying concepts and report analytical expressions for aberration coefficients of arbitrary rotationally symmetric gradient-index (GRIN) lenses. These explicit formulas are well-suited for the application of optimization routines that do not require repeated ray tracing at intermediate optimization steps. We optimize a radial lens, for which we recover a classical solution, and a GRIN lens with inhomogeneous terms along the optical axis.

We describe a method of simulating temporal speckle often encountered in optical lithography. The first step of the method is to generate numerically optical fields with prescribed temporal coherence properties using a shot noise process. By properly arranging the data for instantaneous intensities calculated from the generated fields, one can construct virtually temporal speckle patterns that satisfy all the necessary conditions. As some illustrative examples, we examine the variation of the temporal speckle contrast as a function of the exposure time of the detector by means of these speckle patterns. Our method would provide an intuitive understanding of hardly observable temporal speckle and serve as an educational tool for students and professionals in optics.

The invention of microscopy- and nanoscopy-based imaging technology opened up different research directions in life science. However, these technologies create the need for larger storage space, which has negative impacts on the environment. This scenario creates the need for storing such images in a memory-efficient way. Compact image representation (CIR) can solve the issue as it targets storing images in a memory-efficient way. Thus, in this work, we have designed a deep-learning-based CIR technique that selects key pixels using the guided U-Net (GU-Net) architecture [Asian Conference on Pattern Recognition, p. 317 (2023)], and then near-original images are constructed using a conditional generative adversarial network (GAN)-based architecture. The technique was evaluated on two microscopy- and two scanner-captured-image datasets and obtained good performance in terms of storage requirements and quality of the reconstructed images.

We compare the predictions of two recently derived effective-medium models for the effective refractive index of a turbid suspension of particles. The two formulas are notoriously dissimilar; both are based on the quasi-crystalline approximation, but the approximations used beyond this point are entirely different. Nevertheless, for dilute suspensions both reduce to the well-established van de Hulst formula. The dissimilarities between the formulas are evident for dense suspensions, where dependent-scattering effects are important. When they might coincide is, therefore, not clear. The purpose of this work is to explore the range of particle parameters and volume fractions for which both models are applicable. Our results show that, rather surprisingly, the models produce very similar curves of the real and imaginary parts of the effective refractive index for volume fractions up to 0.4 and for particles comparable to, and larger than, the wavelength, as well as for a fairly large range of refractive-index contrasts between the particles and the surrounding medium. These results significantly increase our confidence in the validity of both models.

Steel surface defects, characterized by multiple types, varied scales, and overlapping occurrences, directly impact the quality, performance, and reliability of industrial products. Proposing a high-precision and high-speed steel surface defect detection algorithm is crucial for ensuring product quality. In this regard, this paper introduces ECM-YOLO, a detection network based on YOLOv8n. First, addressing the insufficient information capture of the C2f module, the C2f enhanced multiscale convolution processing (C2f_EMSCP) module is proposed, enhancing global and local feature capture capabilities through multiscale convolutions. Second, to further enhance the network's robustness and focus on critical information, the channel prior convolutional attention (CPCA) mechanism is integrated between the backbone and neck networks to facilitate more efficient information transmission. Last, a novel, to the best of our knowledge, detection head, i.e., multiscale simple and efficient anchor matching head (MultiSEAMHead), is proposed to mitigate accuracy issues arising from overlaps between different types of defects. Experimental results demonstrate that ECM-YOLO achieves mAPs of 78.9% and 68.2% on the NEU-DET and GC 10-DET data sets, respectively, outperforming YOLOv8n by 2.5% and 4.4%. Moreover, ECM-YOLO excels in model parameters, computational efficiency, and inference speed compared with other models. These findings highlight the applicability of ECM-YOLO for real-time defect detection in industrial settings.