Search Results (108)

Polarimetric descattering imaging has attracted growing interest due to its fundamental physical significance and potential applications. While deep learning has accelerated its development through powerful feature extraction and inference capabilities, existing methods still face limitations in practical scenarios, particularly under dynamic non-uniform scattering conditions such as cement dust environments. To address this, we propose a deep neural network based on the Mueller matrix model that effectively integrates polarization evolution information with deep learning. Specifically, local concentrations of the scattering medium in non-uniform cement dust are characterized by the evolution of the degree of linear polarization (DoLP), which is converted into pixel-wise weight biases to generate customized Mueller matrices adaptable to varying concentrations. The network predicts a pixel-wise dust concentration map and applies the corresponding concentration-specific Mueller matrix to each pixel for polarization-aware dehazing, ensuring physical consistency with Mueller matrix calculus throughout inference. This framework is further enhanced by a physics-constrained optimization loss and multi-scale feature fusion. Experimental results demonstrate the method’s effectiveness and superiority in diverse dynamic non-uniform cement dust environments. Full article

Background: Campylobacter jejuni, a leading foodborne pathogen in poultry, relies heavily on iron for survival and colonizes the gastrointestinal tract (GIT). Iron supplementation in poultry diets can inadvertently promote pathogen growth, particularly when excess or poorly absorbed iron accumulates in the lower GIT. Encapsulated iron products, such as SQM^® Iron, offer a controlled-release mechanism that may mitigate this risk by reducing iron availability to microbes. Objective: This study evaluated the effects of free (FeSO₄) versus polysaccharide–iron complex (PIC) on C. jejuni growth under iron-limited conditions, hypothesizing that encapsulated iron would support slower and more limited bacterial proliferation due to delayed iron release. Methods: Growth kinetics of C. jejuni ATCC 700819 were assessed in chelated Mueller–Hinton broth supplemented with three iron concentrations (10, 20, and 50 ppm) of FeSO₄, PIC, or PIC matrix without iron. Optical density was measured every 20 min over 48 h under microaerophilic conditions. Maximum growth rate (µmax) and carrying capacity (K) were derived using non-linear curve modeling. ANOVA evaluated statistical significance with Tukey’s HSD post hoc comparisons. Results: Free iron (FeSO₄) consistently supported the highest µmax and K values across both trials, indicating rapid and robust C. jejuni proliferation. The effect of encapsulated iron was variable: at higher concentrations (50 ppm) it approached FeSO₄ performance, but at lower concentrations (10 ppm) its effect differed markedly between trials, sometimes supporting growth comparable to free iron and sometimes supporting substantially slower growth. The PIC matrix alone did not promote growth. These variable results indicate that the relationship between encapsulated iron and C. jejuni proliferation is complex and concentration-dependent. Conclusions: Free iron consistently promotes robust C. jejuni growth due to immediate bioavailability. The impact of encapsulated iron on C. jejuni proliferation is nuanced and variable, particularly at lower concentrations, suggesting its role in pathogen control is not straightforward and requires further investigation under controlled conditions. Furthermore, in vivo research is warranted to validate its utility in poultry pathogen management strategies. Full article

We present a numerical study comparing light scattering by small clusters composed of helices, capsules, and spheres. Using the discrete-dipole approximation (DDA), we compute orientation-averaged Mueller-matrix elements M₁₁, M₁₂, and M₁₄ for clusters with varying number of monomers (N = 5–45) and mean center-to-center separation (1–10 particle diameters). Our analysis isolates the influence of particle morphology on angular scattering intensity, linear polarization, and circular intensity differential scattering (CIDS), providing a direct comparison of symmetric and chiral shapes. Helices display persistent angular fine structure in M₁₁ and deep, side-scattering maxima in M₁₂, while spheres and capsules converge to smoother polarization curves with increasing separation. CIDS from symmetric monomers manifests as small oscillations around zero that decay rapidly with monomer separation and number. In contrast, helices produce a stable backward CIDS slope that is largely separation-independent but gradually flattens with increasing number of monomers. These trends confirm that morphology alone can influence key polarization characteristics and provide insights for interpreting scattering from complex-shaped particles. Such morphology-related features may help in the interpretation of polarization data in aerosol and planetary remote sensing and justify the refinement of the design of optical setups for studying irregular or chiral particles in controlled environments. Full article

To date, visual analysis is mainly used to evaluate images of dehydrated films (facies) of biological fluids—microscopy at various magnifications, illumination with white or polarized light, as well as using a dark field. At the same time, important information on the architectonics of optically anisotropic supramolecular networks of facies is unknown (inaccessible). In our work, a model of optical anisotropy of the architectonics of supramolecular networks of blood facies is proposed. Algorithms and a methodology for a new multifunctional method of polarization-interference visualization of the Jones matrix and digital layer-by-layer phase reconstruction of optical anisotropy maps (theziograms) have been developed. As a result, statistically significant markers of oncological changes in the polycrystalline architectonics of supramolecular networks of blood facies samples from healthy donors and patients with papillary thyroid cancer at different stages of the oncological process have been determined and physically analyzed. A comparative study of the diagnostic efficiency of Jones matrix theziography (JT) and Mueller matrix diffusion tomography (MDT) of blood facies samples was conducted within the framework of evidence-based medicine. The main advantages of the Jones matrix method are shown: its multifunctionality (complex detection of birefringence and dichroism), high accuracy of early (stage 1: JM—90.4% and MDT—78.8%) and current (stage 2: JM—96.2% and MDT—88.5%) cancer diagnostics and an excellent level (JM—94.2% and MDT—84.6%) of differentiation of papillary thyroid cancer stages. Full article

At a particular frequency, most materials and objects of interest exhibit a polarization signature, or Mueller matrix, of limited dimensionality, with many matrix elements either negligibly small or redundant due to symmetry. Robust design of a polarization sensor for a particular material or object of interest, or for an application with a limited set of materials or objects, will adapt to the signature subspace, as well as the available modulators, in order to avoid unnecessary measurements and hardware and their associated budgets, errors, and artifacts. At the same time, measured polarization features should be expressed in the Stokes–Mueller basis to allow use of known phenomenology for data interpretation and processing as well as instrument calibration and troubleshooting. This approach to partial Mueller-matrix polarimeter (pMMP) design begins by defining a vector space of reduced Mueller matrices and an instrument vector representing the polarization modulators and other components of the sensor. The reduced-Mueller vector space is proven to be identical to ${\mathbb{R}}^{15}$ and to provide a completely linear description constrained to the Mueller cone. The reduced irradiance, the inner product of the reduced instrument and target vectors, is then applied to construct classifiers and tune modulator parameters, for instance to maximize representation of a specific target in a fixed number of measured channels. This design method eliminates the use of pseudo-inverses and reveals the optimal channel compositions to capture a particular signature feature, or a limited set of features, under given hardware constraints. Examples are given for common optical division-of-amplitude (DoA) 2-channel passive and serial/DoT-DoA 4-channel active polarimeters with rotating crystal modulators for classification of targets with diattenuation and depolarization characteristics. Full article

This study investigates the magneto-optical properties of a ferrofluid using an accessible Ferrocell device. Our findings demonstrate that the ferrofluid’s behavior is critically dependent on its concentration. At high concentrations, the medium is optically dense, with inter-particle scattering and absorption dominating, which prevents the formation of clear light patterns. However, with intermediate dilution, the system enters a “pattern formation zone” where the magnetic field effectively aligns the nanoparticles, creating complex, visible light patterns like horocycles. The appearance of these patterns provides evidence of field-induced ordering and structural coloration. The colors observed are not due to pigments, but result from the interaction of light with the periodic structures formed by the aligned nanoparticles. Our analysis, supported by the Mueller matrix framework, confirms that the ferrofluid acts as a retarder. The birefringence induced by the magnetic field varies across the film, leading to a chromatic dispersion that selectively suppresses certain wavelengths. This process explains how a specific color, such as blue, can be blocked at one location while others pass through, creating structural colors observed in the patterns. Full article

Mueller matrix polarimetry is recently attracting more and more attention for its diagnostic potentials. However, for prevalently used division of time Mueller matrix polarimeter based on dual-rotating retarder scheme, beam drift induced by rotating polarizers and waveplates introduces spatial misalignment and pseudo-edge artifacts in imaging results, hindering following accurate microstructural features characterization. In this paper, we quantitatively analyze the beam drift phenomenon in dual-rotating retarder Mueller matrix microscopy and its impact on linear retardance measurement, which is frequently used to reflect tissue fiber arrangement. It is demonstrated that polarizer rotation induces larger beam drift than waveplate rotation due to surface non-uniformity and stress deformation. Furthermore, for waveplates rotated constantly in dual-rotating retarder scheme, their tilt within polarization state analyzer can result in more drift and throughput loss than those within polarization state generator. Finally, phantom and tissue experiments confirm that beam drift, rather than inherent optical path changes, dominates the systematic overestimation of linear retardance in boundary image regions. The findings highlight beam drift as a dominant error source for quantifying linear retardance, necessitating careful optical design alignment and a reliable registration algorithm to obtain highly accurate polarization data for training machine learning models of pathological diagnosis using Mueller matrix microscopy. Full article

Tongue cancer, the most aggressive subtype of oral cancer, presents critical challenges due to the limited number of specialists available and the time-consuming nature of conventional histopathological diagnosis. To address these issues, we developed an intelligent diagnostic system integrating Mueller matrix microscopy with deep learning to enhance diagnostic accuracy and efficiency. Through Mueller matrix polar decomposition and transformation, micro-polarization feature parameter images were extracted from tongue cancer tissues, and purity parameter images were generated by calculating the purity of the Mueller matrices. A multi-stage feature dataset of Mueller matrix parameter images was constructed using histopathological samples of tongue cancer tissues with varying stages. Based on this dataset, the clinical potential of Mueller matrix microscopy was preliminarily validated for histopathological diagnosis of tongue cancer. Four mainstream medical image classification networks—AlexNet, ResNet50, DenseNet121 and VGGNet16—were employed to quantitatively evaluate the classification performance for tongue cancer stages. DenseNet121 achieved the highest classification accuracy of 98.48%, demonstrating its potential as a robust framework for rapid and accurate intelligent diagnosis of tongue cancer. Full article

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

With the widespread application of optical technology in numerous fields, the polarization performance of transmissive optical components has become increasingly crucial. The extinction ratio, an important indicator for evaluating their polarization characteristics, holds great significance for its precise detection. Aiming at the measurement of the extinction ratio of a transparent component, this study proposes a measurement method for solving the extinction ratio based on measuring the Mueller matrix of the transparent component. The purpose is to analyze the worst position of the extinction ratio of the transmissive component. The extinction ratio of the sample is obtained according to the phase retardation derived from the Stokes vector of the incident light and the Mueller matrix of the optical component, and a theoretical analysis and simulation of this method are carried out. The simulation results verify the feasibility of the theoretical derivation of this method. To further verify the accuracy of the measurement method, experimental verification is conducted. A standard transparent sample with a phase retardation of 13 nm is selected for actual measurement. The data of independent experiments on the transparent sample under different powers are analyzed, and the extinction ratio of the transparent sample is further obtained. When using this method, the relative error is less than 2%, indicating good accuracy. Full article

This paper focuses on the coupling relationships between the optical constants (n: refractive index; k: extinction coefficient) and Mueller matrix elements, as well as between the morphological parameters (σ: root mean square roughness; τ: correlation length) and Mueller matrix elements, of randomly micro-rough surfaces. The electromagnetic response of randomly micro-rough surfaces was simulated by the finite-difference time-domain method, so that the rough surfaces’ reflection coefficients of incident light in the p and s directions could be obtained. According to the formula for the Jones-to-Mueller matrix conversion, we obtained a 4 × 4 Mueller matrix of rough surfaces. The simulation method was validated with experimental results measured by Mueller matrix spectroscopic ellipsometry. It was found that the Mueller matrix element m₁₂ has great potential to invert the optical constants of the rough surfaces, whose refractive indices, n, and extinction coefficients, k, are in the ranges of 0 ≤ n ≤ 4 and 0 ≤ k ≤ 10, respectively. The Mueller matrix element m₃₄ is proportional to the morphological parameters σ/λ (λ: incident wavelength) or σ/τ. Moreover, the expressions (S + β₂) ∝ σ/λ and (S + β₂) ∝ σ/τ can be applied to predict the morphologies of rough surfaces within morphological parameter ranges of 0.003 ≤ σ/λ ≤ 0.015 and 0.125 ≤ σ/τ ≤ 0.75. This research signifies a key step toward the ability to invert the morphological parameters or optical constants of micro-rough surfaces through a Mueller matrix. Full article

Mueller matrix polarimetry has great potential for tissue detection and clinical diagnosis due to its ability to provide rich microstructural information accurately. However, in practical in vivo tissue imaging based on backscattering Mueller matrix polarimetry, specular reflection is often inevitable, leading to overexposed regions and the following inaccurate polarization information acquisition of tissues. In this study, we probe the influence of specular reflection and overexposure on backscattering Mueller matrix polarimetry for tissue imaging and sensing. We investigate in detail the differentiation of polarization behaviors between the specular reflection and non-specular reflection tissue regions using a 3 × 3 backscattering Mueller matrix measurement. Then, we obtain the vertical projection profiles to further quantify the Mueller matrix elements of porcine liver tissue in different specular reflection regions. Finally, we calculate the polarization feature parameters derived from a 3 × 3 Mueller matrix and analyze their behavior in overexposed regions. Based on the quantitative analysis and comparisons, we obtain a group of polarization feature parameters with strong immunity to the specular reflection process. This study offers a strategy for selecting the polarization parameters during in vivo polarimetric imaging applications, provides valuable references for further eliminating the characterization errors induced by specular reflection, and may contribute to the advancement of quantitative tissue polarimetric imaging and sensing. Full article

Underwater polarization imaging has emerged as a fundamental technique for detecting and imaging underwater targets. However, the effectiveness of this technique is hampered by the low light intensity and optical system deformation induced by water pressure in deep-water environments, particularly for the detection of polarized signals. To address this issue, a wide-field-of-view oil-immersion lens tailored for deep-sea operations is designed, offering robust imaging performance and an extensive observation range. A Mueller matrix is deployed to scrutinize the polarization properties of the entire optical system across diverse fields of view, and the measurement errors in the polarization degree under incident polarization states are discussed. Simulation results demonstrate that the measurement error for linearly polarized light is greater than that for circularly polarized light. Therefore, the system adopts circularly polarized light as the active illumination source, characterized by minimal polarization effects and high detection accuracy. Finally, a deep-sea camera lens is produced and manufactured. The resulting lens is shown to pass a test in a hydrodynamic simulator machine, demonstrating that it can operate properly and capture images. Full article

Due to the limited accuracy of experimental data, Mueller polarimetry can produce real 4×4 matrices that fail to meet required covariance or passivity conditions. A general and simple procedure to convert any real 4×4 matrix into a valid Mueller matrix by adding a portion of polarimetric white noise is presented. This approach provides deeper insight into the structure of Mueller matrices and has a subtle relation to the effective component of the Mueller matrix, which is defined through the subtraction of the fully random component of the characteristic decomposition. Up to a scale coefficient determined by the third index of polarimetric purity of the original Mueller matrix, the effective component retains complete information on the polarimetric anisotropies. Full article

We study the problem of classification of various real-world objects using as input a database (DB) of laboratory polarimetric measures (Mueller matrix elements—MMEs). It can work as a complementary technology of surroundings’ imaging that can be used, in particular, in autonomous driving. To this end, we look for an algorithm using less input parameters without great loss of the quality of classification. We start by analyzing the data in order to understand the attributes that are more important for associating the objects with one of several predefined classes. Different sets of attributes are studied using an artificial neural network (ANN), which is optimized in terms of the number of hidden layers and the activation function. After that, an improved machine learning (ML) architecture is built using the K-nearest neighbors (KNN) classifier on each cluster generated by applying the pre-trained ANN to the training set. This article focuses on the situation wherein one may not be able to measure all MMEs or it would be too expensive or challenging to implement when the measurement time is crucial. The results obtained for a reduced set of attributes using different ML architectures are very good, especially for the proposed combined ANN-KNN approach (wherein the ANN acts as a predictor and KNN as a corrector), which can help to avoid measuring all MMEs. Full article