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. This research further develops the prior findings regarding dichromats' discrimination of illumination variations under simulated daylight conditions in image analysis. Applying the cone-contrast metric to compare thresholds between changes in bluer/yellower daylight and unnatural red/green changes, we propose a weak preservation of sensitivity to daylight alterations in X-linked CVDs.
Within the context of underwater wireless optical communication systems (UWOCSs), vortex X-waves coupled with orbital angular momentum (OAM) and spatiotemporal invariance are now being investigated. Employing the Rytov approximation and correlation function, we ascertain the OAM probability density of vortex X-waves and the UWOCS channel capacity. Moreover, a thorough examination of OAM detection likelihood and channel capacity is conducted on vortex X-waves conveying OAM within anisotropic von Kármán oceanic turbulence. The results demonstrate that a rise in the OAM quantum number brings about a hollow X structure in the receiving plane, where the energy of vortex X-waves is funneled into the lobes, lessening the probability of vortex X-waves being received. As the Bessel cone angle expands, the energy distribution becomes increasingly centered, and the vortex X-waves become more compact. Our research into OAM encoding may serve as a catalyst for the creation of UWOCS, a system designed for transferring large volumes of data.
For colorimetric characterization of the wide-gamut camera, we suggest modeling the color conversion between the camera's RGB space and the CIEXYZ space of the CIEXYZ standard, using a multilayer artificial neural network (ML-ANN) with the error-backpropagation algorithm. The ML-ANN's model architecture, forward propagation methodology, error backpropagation algorithm, and training policy are discussed in this paper. The creation of wide-color-gamut datasets for machine learning (ML-ANN) model training and evaluation was detailed, leveraging the spectral reflection data of ColorChecker-SG blocks alongside the spectral sensitivity profiles of RGB camera systems. During this time, diverse polynomial transforms were employed in a comparative experiment alongside the least-squares method. Experiments show an evident decrease in both training and testing errors, a result of augmenting both the number of hidden layers and the number of neurons per hidden layer. The optimal hidden layer configuration of the ML-ANN has demonstrably decreased mean training and testing errors to 0.69 and 0.84 (CIELAB color difference), respectively, representing a superior outcome to all polynomial transformations, including the quartic.
We investigate the evolution of the state of polarization (SoP) within a twisted vector optical field (TVOF) with an astigmatic phase, propagating through a strongly nonlocal nonlinear medium (SNNM). An astigmatic phase's impact on the propagation dynamics of the twisted scalar optical field (TSOF) and TVOF within the SNNM yields a periodic alternation of stretching and compressing, accompanied by a reciprocal evolution between a circular and a thread-like beam shape. CI1040 If the beams exhibit anisotropy, the TSOF and TVOF will rotate about the propagation axis. The TVOF demonstrates reciprocal transformations of linear and circular polarizations during propagation, these conversions being noticeably affected by the initial power amounts, twisting strength factors, and initial beam modifications. The moment method's analytical predictions for the dynamics of TSOF and TVOF, as they propagate in a SNNM, are substantiated by the numerical results. In-depth analysis of the underlying physical principles governing polarization evolution for a TVOF within a SNNM is provided.
Earlier studies have shown that the shape of objects is pivotal to interpreting the quality of translucency. How semi-opaque objects are perceived is examined in this study, focusing on the effect of surface gloss. We experimented with different specular roughness values, specular amplitude levels, and simulated light source directions to illuminate the globally convex bumpy object. An increase in specular roughness corresponded with a rise in perceived lightness and surface roughness. Diminishing levels of perceived saturation were observed, though the magnitude of these declines proved comparatively negligible alongside these enhancements in specular roughness. Inverse correlations were identified among perceived lightness and gloss, perceived saturation and transmittance, and perceived gloss and roughness. Positive correlations were discovered, connecting perceived transmittance with glossiness and perceived roughness with perceived lightness. Specular reflections' effect extends beyond perceived gloss, impacting the perception of both transmittance and color attributes, as these findings indicate. 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. The influence of lighting direction on perceived transmittance, as observed in our study, points to intricate perceptual processes needing a deeper investigation.
For morphological analysis of biological cells using quantitative phase microscopy, measuring the phase gradient is essential. We introduce a deep learning method in this paper to directly compute the phase gradient, dispensing with phase unwrapping and numerical differentiation. Numerical simulations, featuring substantial noise levels, confirm the proposed method's robustness. Moreover, we showcase the method's applicability in visualizing diverse biological cells through a diffraction phase microscopy configuration.
The development of various statistical and learning-based methods for illuminant estimation has been driven by significant efforts in both academia and industry. While not insignificant for smartphone camera capture, images featuring a single color (i.e., pure color images) have, however, been overlooked. This research effort resulted in the creation of the PolyU Pure Color dataset, specifically designed for pure color images. A lightweight multilayer perceptron (MLP) neural network model, named 'Pure Color Constancy' (PCC), was likewise developed for the task of determining the illuminant in pure-color images. This model extracts and utilizes four color features: the chromaticities of the maximal, average, brightest, and darkest image pixels. The PCC method, when applied to pure color images in the PolyU Pure Color dataset, showed considerable improvement over existing learning-based methods. Comparable results were obtained with standard datasets and demonstrated a good cross-sensor performance. An impressive performance was attained using a significantly smaller parameter count (approximately 400) and a remarkably brief processing time (around 0.025 milliseconds) for an image, all executed with an unoptimized Python package. Practical implementation of the proposed method is made feasible.
To navigate safely and comfortably, there needs to be a noticeable variation in appearance between the road and its markings. This contrast can be better achieved by utilizing optimized road illumination designs, employing luminaires with particular luminous intensity patterns, and making the most of the road's (retro)reflective properties and markings. Little is known about the retroreflective characteristics of road markings for incident and viewing angles pertinent to street luminaires. To address this knowledge gap, the bidirectional reflectance distribution function (BRDF) values of various retroreflective materials are determined across a broad spectrum of illumination and viewing angles using a luminance camera within a commercial near-field goniophotometer setup. A new, optimized RetroPhong model successfully fits the experimental data, demonstrating strong correlation with the observed values (root mean squared error (RMSE) 0.8). Benchmarking the RetroPhong model against comparable retroreflective BRDF models indicates its superior performance for the current samples and measurement environment.
For optimal performance in both classical and quantum optics, a device with dual functionality as a wavelength beam splitter and a power beam splitter is desired. A phase-gradient metasurface in both the x and y axes is used to create a triple-band, large-spatial-separation beam splitter for visible wavelengths. At normal incidence with x-polarization, the blue light undergoes splitting into two equal-intensity beams along the y-axis, a consequence of resonance within a single meta-atom; in contrast, the green light splits into two equal-intensity beams aligned with the x-axis due to variations in size between adjacent meta-atoms; the red light, however, remains unsplit, traversing directly through the structure. Their phase response and transmittance were the determining factors in optimizing the meta-atoms' size. At a normal angle of incidence, the simulated working efficiencies for wavelengths of 420 nm, 530 nm, and 730 nm are 681%, 850%, and 819%, respectively. Biopsy needle A discussion of the sensitivities associated with oblique incidence and polarization angle is also provided.
Compensating for anisoplanatism in wide-field imaging through atmospheric media generally calls for a tomographic reconstruction of the turbulent volume. standard cleaning and disinfection To reconstruct the data, the turbulence volume must be estimated, modeled as a profile composed of numerous thin, homogeneous layers. Using wavefront slope measurements, the signal-to-noise ratio (SNR) for a layer of uniform turbulence, which indicates the level of difficulty of detection, is presented.