Using a standard CIELUV metric and a cone-contrast metric developed for distinct types of color vision deficiencies (CVDs), our results indicate that discrimination thresholds for changes in daylight do not differ between normal trichromats and individuals with CVDs, such as dichromats and anomalous trichromats; however, significant differences in thresholds emerge under non-standard illuminations. The prior report on the illumination discrimination aptitude of dichromats in simulated daylight images is enhanced by this new result. 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.
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. We calculate the OAM probability density of vortex X-waves and the UWOCS channel capacity by leveraging the Rytov approximation and the correlation function. Finally, a thorough study of OAM detection probability and channel capacity is applied to vortex X-waves transporting OAM in anisotropically structured von Kármán oceanic turbulence. The outcome indicates that an expansion in OAM quantum numbers generates a hollow X-shape within the plane of reception. The energy of vortex X-waves is injected into the lobes, thereby reducing the probability of the transmitted vortex X-waves arriving at the receiving end. A widening of the Bessel cone angle causes the energy to increasingly cluster around the energy distribution center, and the vortex X-waves to display a more restricted spatial pattern. The development of UWOCS, a system for bulk data transfer employing OAM encoding, could be a consequence of our research.
In order to achieve colorimetric characterization for a camera featuring a wide color gamut, we advocate for utilizing a multilayer artificial neural network (ML-ANN), coupled with the error-backpropagation algorithm, to model color conversions between the camera's RGB space and the CIEXYZ space of the CIEXYZ standard. This paper introduces the ML-ANN's architectural framework, its forward calculation model, its error backpropagation mechanism, and its learning policy. The spectral reflectance curves of ColorChecker-SG blocks, combined with the spectral sensitivity curves of typical RGB camera channels, informed the development of a method for creating wide-color-gamut samples for the training and evaluation of ML-ANN models. A comparative experiment employing the least-squares method with diverse polynomial transformations was conducted concurrently. Substantial reductions in both training and testing errors are observed in the experimental results when increasing the number of hidden layers and neurons in each hidden layer. Using optimal hidden layers, the mean training error and mean testing error of the ML-ANN have been decreased to 0.69 and 0.84, respectively, resulting in a significant improvement over all polynomial transformations, including the quartic, in terms of (CIELAB color difference).
A detailed analysis of the state of polarization (SoP) evolution in a twisted vector optical field (TVOF) exhibiting astigmatic phase, while interacting with a strongly nonlocal nonlinear medium (SNNM), is presented. The twisted scalar optical field (TSOF) and TVOF's propagation in the SNNM, influenced by an astigmatic phase, shows a reciprocating pattern of expansion and contraction, accompanied by the conversion from a circular to a filamentous beam distribution. ART0380 datasheet The TSOF and TVOF's rotation around the propagation axis is conditional upon the beams' anisotropy. The TVOF's propagation dynamics involve reciprocal polarization shifts between linear and circular forms, directly tied to the initial power levels, twisting force coefficients, and the starting beam shapes. In a SNNM, the numerical results provide corroboration for the moment method's analytical predictions on the dynamic behavior of TSOF and TVOF during their propagation. The detailed physics of polarization evolution in a TVOF system, situated within a SNNM environment, are scrutinized.
Information regarding the shape of objects, according to prior studies, is a critical element in recognizing translucency. We examine in this study the manner in which semi-opaque object perception is modulated by the degree of surface gloss. The specular roughness, specular amplitude, and the light source's simulated direction were altered to illuminate the globally convex, bumpy object. We observed a correlation between escalating specular roughness and a subsequent increase in perceived lightness and surface texture. Decreases in the perception of saturation were observed, yet these decreases exhibited a much smaller magnitude compared to the increases in specular roughness. Perceived gloss exhibited an inverse correlation with perceived lightness, while perceived transmittance inversely correlated with perceived saturation, and perceived roughness showed an inverse relationship with perceived gloss. Perceived transmittance was positively correlated with glossiness, and perceived roughness was positively correlated with perceived lightness. Beyond perceived gloss, the impact of specular reflections extends to the perception of transmittance and color characteristics, as indicated by these findings. Subsequent modeling of image data revealed that the perceived saturation and lightness were related to the use of image regions with greater chroma and lower lightness, respectively. Perceived transmittance, we found, is demonstrably influenced by systematic variations in lighting direction, suggesting intricate perceptual relationships demanding further investigation.
Quantitative phase microscopy hinges on the accurate measurement of the phase gradient for effective biological cell morphological studies. Our proposed method, built on a deep learning framework, directly estimates the phase gradient without recourse to phase unwrapping or numerical differentiation. Under conditions of extreme noise, the robustness of the proposed method is showcased through numerical simulations. Moreover, we showcase the method's applicability in visualizing diverse biological cells through a diffraction phase microscopy configuration.
Extensive efforts in both academic and industrial contexts have contributed to the development of numerous statistical and machine learning-based techniques for illuminant estimation. While not insignificant for smartphone camera capture, images featuring a single color (i.e., pure color images) have, however, been overlooked. This research project saw the development of the PolyU Pure Color dataset, dedicated to pure color imagery. 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. In the PolyU Pure Color dataset, the proposed PCC method demonstrated significantly superior performance compared to other state-of-the-art learning-based approaches when applied to pure color images. Across two standard image datasets, its performance was comparable, along with displaying a robust cross-sensor performance. Surprisingly good performance was observed with a substantially fewer parameters (about 400) and an exceptionally short processing time (around 0.025 milliseconds) when processing an image using an unoptimized Python library. Real-world implementation of this proposed method is now within reach.
To navigate safely and comfortably, there needs to be a noticeable variation in appearance between the road and its markings. By improving road lighting design and deploying luminaires with targeted luminous intensity distributions, this contrast can be strengthened by effectively utilizing the (retro)reflective properties of the road surface and markings. Concerning the (retro)reflective properties of road markings under the incident and viewing angles significant for street lighting, only scant information is available. Therefore, the bidirectional reflectance distribution function (BRDF) values of certain retroreflective materials are quantified for a wide range of illumination and viewing angles employing a luminance camera in a commercial near-field goniophotometer setup. A well-optimized RetroPhong model accurately represents the experimental data, showing a high degree of agreement with the findings (root mean squared error (RMSE) = 0.8). The RetroPhong model stands out among other relevant retroreflective BRDF models, exhibiting the most suitable results for the current sample set and measurement conditions.
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 large-spatial-separation beam splitter with triple-band operation at visible wavelengths is presented, utilizing a phase-gradient metasurface in both the x- and y-directions. 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. To optimize the size of the meta-atoms, their phase response and transmittance were considered. Under normal incidence, the simulated working efficiencies for wavelengths 420 nm, 530 nm, and 730 nm are 681%, 850%, and 819% respectively. ART0380 datasheet The influence of oblique incidence and polarization angle sensitivities is also examined.
Compensating for anisoplanatism in wide-field imaging through atmospheric media generally calls for a tomographic reconstruction of the turbulent volume. ART0380 datasheet The reconstruction procedure requires the quantification of turbulence volume, which is represented by a profile of thin, homogeneous layers. A layer's signal-to-noise ratio (SNR), a parameter that reflects the difficulty of detecting a homogeneous turbulent layer through wavefront slope measurements, is presented.