Our research, employing both a standard CIELUV metric and a cone-contrast metric optimized for various color vision deficiencies (CVDs), demonstrates no difference in discrimination thresholds for variations in daylight between normal trichromats and individuals with CVDs, such as dichromats and anomalous trichromats. However, there is a significant difference in thresholds when assessing atypical lighting. A preceding study on dichromats' skill in perceiving illumination variations in simulated daylight conditions in images is strengthened by this supplementary report. Employing the cone-contrast metric to assess threshold differences between bluer/yellower and unnatural red/green daylight shifts, we hypothesize a slight preservation of daylight sensitivity in X-linked CVDs.

Vortex X-waves, with their coupling to orbital angular momentum (OAM) and spatiotemporal invariance, are now a significant element in research on underwater wireless optical communication systems (UWOCSs). We calculate the OAM probability density of vortex X-waves and the UWOCS channel capacity by leveraging the Rytov approximation and the correlation function. Further, a deep dive into the detection likelihood of OAM and channel capacity is undertaken on vortex X-waves transmitting OAM within anisotropic von Kármán oceanic turbulence. The OAM quantum number's elevation yields a hollow X-form in the receiving plane, where vortex X-wave energy is channeled into lobes, thereby diminishing the probability of vortex X-waves reaching the receiving end. Increasing the Bessel cone angle leads to a progressive focusing of energy around its central distribution point, and the vortex X-waves exhibit enhanced localization. The development of UWOCS for bulk data transfer, utilizing OAM encoding, may be spurred by our research.

To achieve colorimetric characterization for the camera with an expansive color gamut, we propose employing a multilayer artificial neural network (ML-ANN), trained using the error-backpropagation algorithm, to model the color transformation from the camera's RGB space to the CIEXYZ standard's XYZ space. This document outlines the design of the ML-ANN, including its architecture, forward calculation procedure, error backpropagation method, and training strategy. Leveraging the spectral reflectance curves of ColorChecker-SG blocks and the spectral sensitivity functions of standard RGB camera sensors, a method for the generation of wide color gamut samples for ML-ANN training and validation was outlined. The least-squares method was used, alongside various polynomial transformations, in a comparative experiment which took place during this period. Analysis of the experimental data reveals a discernible decrease in training and testing errors when increasing the number of hidden layers and the number of neurons within each hidden layer. Significant reductions in mean training and testing errors have been observed in the ML-ANN with optimal hidden layers, yielding values of 0.69 and 0.84, respectively (CIELAB color difference). This improvement is substantial compared to every polynomial transformation, including the quartic.

Polarization state evolution (SoP) is studied in a twisted vector optical field (TVOF), incorporating an astigmatic phase, as it propagates through a strongly nonlocal nonlinear medium (SNNM). Propagation through the SNNM of the twisted scalar optical field (TSOF) and TVOF, impacted by an astigmatic phase, induces a periodic interplay of elongation and contraction, coupled with a reciprocal alteration of the beam's initial circular form into a thread-like structure. AG-14361 research buy The propagation axis witnesses the rotation of the TSOF and TVOF, contingent upon the anisotropy of the beams. Specifically, the reciprocal transformations between linear and circular polarizations transpire within the TVOF throughout propagation, exhibiting a strong dependence on initial power levels, twisting coefficient strengths, and the initial beam configurations. The moment method's analytical predictions regarding TSOF and TVOF dynamics are confirmed through numerical results, specifically during propagation in a SNNM. In-depth analysis of the underlying physical principles governing polarization evolution for a TVOF within a SNNM is provided.

Past investigations have demonstrated that details about the form of objects play a crucial role in our understanding of translucency. The perception of semi-opaque objects is scrutinized in this research, with a particular emphasis on variations in surface gloss. We explored the effects of varying specular roughness, specular amplitude, and the simulated light source's direction on the globally convex, bumpy object. Our findings demonstrate a positive relationship between specular roughness and the amplified perception of both surface lightness and roughness. Despite the observable decrease in perceived saturation, the declines were considerably less significant when paired with increases in specular roughness. Lightness and gloss, saturation and transmittance, as well as roughness and gloss, were discovered to have inverse correlations. Perceived transmittance and glossiness exhibited a positive correlation, mirroring the positive correlation found between perceived roughness and perceived lightness. The influence of specular reflections extends to the perception of transmittance and color attributes, not merely the perception of gloss, as suggested by these findings. A follow-up analysis of image data demonstrated that perceived saturation and lightness could be explained by the reliance on different image regions that have varying chroma and lightness, respectively. A systematic correlation between lighting direction and perceived transmittance was identified, implying the need for more consideration of the complex perceptual interactions that underly this effect.

Phase gradient measurement plays a significant role in quantitative phase microscopy for understanding the morphology of biological cells. This paper introduces a deep learning technique for direct phase gradient estimation, thereby avoiding the complexities of phase unwrapping and numerical differentiation. Numerical simulations under severe noise illustrate the robust performance of the proposed method. In addition, the method's use for imaging diverse types of biological cells is illustrated using a diffraction phase microscopy setup.

The development of diverse statistical and learning-based methods for illuminant estimation has resulted from substantial contributions from both academic and industrial sectors. Despite their non-trivial nature for smartphone cameras, images dominated by a single hue (i.e., pure color images) have received scant attention. In the course of this study, the PolyU Pure Color dataset, consisting of images with pure colors, was established. A feature-based multilayer perceptron (MLP) neural network, abbreviated 'Pure Color Constancy' (PCC), was also developed to estimate the illuminant in pure-color images. The model uses four color features extracted from the image: the chromaticities of the maximum, mean, brightest, and darkest pixels. For pure color images in the PolyU Pure Color dataset, the proposed PCC method significantly surpassed the performance of competing learning-based methods. Across two other image datasets, its performance was comparable and displayed consistent performance across different sensors. The image achieved excellent performance metrics with an unusually small parameter set (around 400) and a remarkably quick processing time (approximately 0.025 milliseconds), despite being processed using an unoptimized Python library. This proposed method enables the practical deployment of the solution.

To navigate safely and comfortably, there needs to be a noticeable variation in appearance between the road and its markings. Optimizing road illumination through carefully designed luminaires with specific luminous intensity patterns can enhance this contrast by leveraging the (retro)reflective qualities of the road surface and markings. Given the limited understanding of road markings' (retro)reflective properties for incident and viewing angles crucial to streetlight design, the bidirectional reflectance distribution function (BRDF) values of selected retroreflective materials are measured over a wide range of illumination and viewing angles with a luminance camera in a commercial, close-proximity goniophotometer configuration. An optimized RetroPhong model demonstrates excellent agreement with the experimental data; the root mean squared error (RMSE) is 0.8. The RetroPhong model's performance, when measured against other relevant retroreflective BRDF models, highlights its effectiveness with the current sample set and measurement conditions.

Classical and quantum optics alike necessitate a component that embodies both wavelength beam splitting and power beam splitting capabilities. For visible wavelengths, we propose a triple-band beam splitter with large spatial separation, constructed using a phase-gradient metasurface in both the x- and y-directions. The blue light, subject to x-polarized normal incidence, is split into two equal-intensity beams along the y-axis due to resonance within an individual meta-atom; the green light, similarly subjected to the same incidence, splits into two beams of identical intensity in the x-direction because of the varying sizes between adjacent meta-atoms; and the red light maintains its path uninterrupted without splitting. An optimization process for the size of the meta-atoms was based on evaluating their phase response and transmittance. The simulated working efficiencies under normal incidence at 420 nm, 530 nm, and 730 nm are 681%, 850%, and 819% respectively. AG-14361 research buy Furthermore, the sensitivities exhibited by oblique incidence and polarization angle are detailed.

Compensating for anisoplanatism in wide-field imaging through atmospheric media generally calls for a tomographic reconstruction of the turbulent volume. AG-14361 research buy Reconstructing the data depends on estimating turbulence volume, conceptualized as a profile comprised of multiple thin, homogeneous layers. We introduce the signal-to-noise ratio (SNR) value for a layer, a measure indicating the difficulty of detecting a single layer of uniform turbulence with wavefront slope measurements.