Search Results (196)

In medical endoscopic imaging, specular reflection (SR) frequently leads to local overexposure, obscuring essential tissue information and complicating computer-aided diagnosis (CAD). Traditional convolutional neural networks (CNNs) face difficulties in modeling global illumination phenomena due to their biased local receptive fields and the inherent “object assumption.” Conversely, pure transformer models often lose high-frequency boundary details and incur substantial computational costs. To tackle these challenges, this paper introduces VDTAR-Net, a specialized framework adapted to address the unique optical characteristics of specular reflections. Building upon hybrid architectures, our contribution focuses on two core mechanisms: (1) a Cross-architecture Fusion Module (CFM) that enables deep, bidirectional information flow, allowing the Transformer’s global illumination modeling to continuously correct the CNN’s local texture biases; and (2) a Reflective-Aware Module (RAM), which explicitly integrates the physical prior of high-intensity saturation into the attention mechanism. This task-specific design significantly enhances sensitivity to boundary details in overexposed regions. We also created the first large-scale, expert-labeled cervical white light segmentation dataset, Cervix-WL-900. High-quality ground truth labels were generated through rigorous double-blind annotation and arbitration by senior experts. Experimental results show that VDTAR-Net achieves a Dice score of 92.56% and a mean Intersection over Union (mIoU) score of 87.31% on Cervix-WL-900, demonstrating superior performance compared to methods like U-Net, DeepLabv3+, SegFormer, and PSPNet. Ablation studies further confirm the substantial contributions of dual-path collaboration, CFM deep fusion, and RAM task-specific priors. VDTAR-Net provides a robust baseline for precise highlight segmentation, laying a foundation for subsequent image quality assessment, restoration, and feature decoupling in diagnostic models. Full article

Machined surfaces contain rich information about machining conditions and system behavior and are typically assessed using off-line, small-area metrology. This study developed and validated an image-based methodology for process-oriented surface texture analysis of end-milled Spheroidal Graphite Iron (SGI), enabling scalable, non-contact monitoring suitable for in-line deployment. End milling trials were conducted under optimized and aggressive cutting conditions and in two orthogonal feed directions (X,Y). Surface topography from White Light Interferometry (WLI) was complemented by Charge-Coupled Device (CCD) microscope imaging. Image processing comprised automatic orientation correction, intensity profile extraction, and frequency-domain analysis via Fast Fourier Transform and power spectral density estimation. Texture metrics (RMS amplitude, skewness, kurtosis, dominant wavelength) were derived from intensity profiles, and two spectral indices were introduced: a Change Index (CI), capturing high-frequency content linked to process disturbances, and a Surface Anisotropy Metric (SAM), quantifying texture directionality. Aggressive cutting increased RMS by 28.5% and shifted skewness by 274% with strong statistical significance. Directional analysis showed 22% higher texture amplitude in Y than X, indicating axis-dependent machine behavior. CI correlated with the machining parameters and stability, while SAM reflected the machine and setup characteristics. Trends were consistent with WLI, supporting the method as a rapid, complementary tool for surface quality and machine condition monitoring. Full article

Time-resolved spectroscopy (TRS) is a promising tool for noninvasive cerebral monitoring in neonates. However, the optimal forehead site for probe placement remains unclear. In this study, we evaluated the effect of probe positioning on TRS-derived optical parameters in neonates. TRS measurements were obtained from the midline and right lateral forehead of 30 neonates (≥36 weeks’ corrected gestational age). We compared various parameters between the two probe positions, including optical intensity, attenuation, mean optical path length, scattering coefficient, total hemoglobin (tHb), cerebral oxygen saturation (ScO₂) and cerebral blood volume (CBV). No significant differences were observed in tHb, ScO₂ and CBV between the midline and lateral sites. However, the lateral site showed a significantly lower scattering coefficient and shorter mean path length. Light intensity was increased and attenuation was reduced at the lateral site. Thus, while tHb, ScO₂ and CBV values were consistent between sites, the midline provided more stable scattering and optical path data. These findings suggest that the midline forehead may be a more suitable site for TRS-based neonatal cerebral monitoring. Full article

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R² of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes. Full article

Efficient and reliable road damage detection is a critical component of intelligent transportation and infrastructure control systems that rely on visual sensing technologies. Existing road damage detection models are facing challenges such as missed detection of fine cracks, poor adaptability to lighting changes, and false positives under complex backgrounds. In this study, we propose an enhanced YOLO-based framework, YOLO-ERCD, designed to improve the accuracy and robustness of sensor-acquired image data for road crack detection. The datasets used in this work were collected from vehicle-mounted and traffic surveillance camera sensors, representing typical visual sensing systems in automated road inspection. The proposed architecture integrates three key components: (1) a residual convolutional block attention module, which preserves original feature information through residual connections while strengthening spatial and channel feature representation; (2) a channel-wise adaptive gamma correction module that models the nonlinear response of the human visual system to light intensity, adaptively enhancing brightness details for improved robustness under diverse lighting conditions; (3) a visual focus noise modulation module that reduces background interference by selectively introducing noise, emphasizing damage-specific features. These three modules are specifically designed to address the limitations of YOLOv10 in feature representation, lighting adaptation, and background interference suppression, working synergistically to enhance the model’s detection accuracy and robustness, and closely aligning with the practical needs of road monitoring applications. Experimental results on both proprietary and public datasets demonstrate that YOLO-ERCD outperforms recent road damage detection models in accuracy and computational efficiency. The lightweight design also supports real-time deployment on edge sensing and control devices. These findings highlight the potential of integrating AI-based visual sensing and intelligent control, contributing to the development of robust, efficient, and perception-aware road monitoring systems. Full article

Intrinsic optical bi-stability in dense two-level systems is developed for the bad cavity limit where electromagnetic modes are adiabatically eliminated. Each atom interacts via dipole–dipole forces with its nearby spatial distribution of atoms. The theory is developed into two parts, corresponding to the short sample, with dimensions shorter than the wavelength, and the long sample. In both cases, the local field corrections modify the Maxwell–Bloch equations, so that cubic or quartic equations are obtained for the inversion of population as a function of the external light intensity, thus leading to intrinsic bi-stability. The effects of noise sources on intrinsic bi-stability were treated, and I found that while the observability of bi-stability was not obtained experimentally for a simple two-level system, there were many observations of bi-stability obtained through the ‘up-conversion’ of rare earth excited crystals. I show the differences between these two systems. Full article

Since the early development of Fluorescence Correlation Spectroscopy (FCS), it has been recognized that background intensity can lead to artifacts in the amplitude of the autocorrelation function (ACF) and, consequently, to inaccurate estimates of particle numbers. Here, we present a protocol for quantitative background evaluation and amplitude correction in FCS experiments, applicable to different sources of background such as detector noise, autofluorescence, and light scattering. We demonstrate the performance of our approach through three representative case studies: (i) FCS measurements of a bright fluorophore at low concentration, (ii) FCS of dim nanoparticles affected by solvent Raman scattering, and (iii) FCS performed using a confocal setup equipped with a SPAD array, where background originates from detector hot pixels. These examples represent typical experimental conditions in which background signals compromise quantitative interpretation, illustrating how our protocol restores accuracy and reproducibility in FCS analysis. By systematically identifying and correcting these effects, the proposed protocol addresses a long-standing limitation of FCS and provides a robust framework for improving the accuracy and reproducibility of quantitative fluorescence measurements. Full article

Circadian rhythm disruption induced by exposure to light—excessive in duration and intensity (dark deprivation)—and the impact of hepatotoxins are both significant risk factors for liver pathology. The purpose of this research was to evaluate the potentially synergistic effects of continuous lighting and carbon tetrachloride (CCl₄) toxicity on the structural and functional organization and daily (circadian) rhythmicity of the liver in rats, as well as to look at the corrective capability of exogenous melatonin under such influences. The experiment was conducted on 200 outbred 6-month-old Wistar rat males, which were distributed into five groups, including a control (normal light/dark cycle), dark deprivation (constant light), CCl₄ intoxication, and combined exposure to CCl₄ and dark deprivation with or without melatonin administration (0.3 mg/kg). Histological, immunohistochemical (Ki-67, Per2, and Bmal1), biochemical, and ELISA methods were used. Circadian rhythms were analyzed using cosinor. It was shown that dark deprivation and CCl₄ intoxication act synergistically, potentiating liver damage. The most severe necrosis (54.17 ± 9.13%), steatosis (57.85 ± 12.14%), and suppression of regenerative potential (decreased proportion of binucleated hepatocytes to 2.17 ± 0.21%) were observed in the group with combined exposure. This correlated with a substantial decline in melatonin content in blood plasma (7.85 ± 2.1 pg/mL) and a profound disruption in circadian rhythms. Administration of exogenous melatonin exerted pronounced hepatoprotective and chronotropic effects: it significantly reduced pathological changes (necrosis reduced to 16.35 ± 6.17%), stimulated regeneration (binucleated hepatocytes increased to 13.57 ± 0.81%), and restored the circadian rhythms of the studied parameters to levels close to those of the control. The key pathogenetic link in the potentiation of CCl₄ hepatotoxicity under dark deprivation is light-induced deficiency of endogenous melatonin. Exogenous melatonin demonstrated high efficacy in correcting both structural and functional damage and liver desynchronosis, confirming its therapeutic potential under conditions of combined exposure to chronodisruptors and toxins. Full article

The Photonic Lantern (PL) is a novel fiber optic device emerging in wavefront sensing, which converts multimode fiber light fields into single-mode fields. By decomposing complex multimode fields into simple fundamental modes, the PL maps wavefront aberrations to light intensity. The Photonic Lantern Wavefront Sensor (PLWFS) functions as an ideal focal-plane sensor. It aligns the focal and imaging planes to coincide completely. This configuration mitigates Non-Common Path Aberrations (NCPAs), which traditional sensors struggle to resolve. This paper reviews the research history of the PLWFS. It first introduces the fabrication methods for PL, then focuses on illustrating the theoretical and experimental developments of the PLWFS. PLWFS research began with the initial realization of sensing simple tip/tilt aberrations, moved to establishing linear response models for small aberrations, and subsequently introduced methods such as neural network algorithms and broadband polychromatic light sources to achieve large aberration sensing and correction. This paper highlights significant research achievements from each stage, summarizes the current limitations in the research, and finally discusses the future potential of the PLWFS as an excellent focal-plane wavefront sensor. Full article

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

Agricultural drainage canals provide critical habitats for fish species that are highly sensitive to agricultural practices. However, conventional monitoring methods such as capture surveys are invasive and labor-intensive, which means they can disturb fish populations and hinder long-term ecological assessment. Therefore, there is a strong need for effective and non-invasive monitoring techniques. In this study, we developed a practical method using the YOLOv8n deep learning model to automatically detect and quantify fish occurrence in underwater images from a canal in Ibaraki Prefecture, Japan. The model showed high performance in validation (F1-score = 91.6%, Precision = 95.1%, Recall = 88.4%) but exhibited reduced performance under real field conditions (F1-score = 61.6%) due to turbidity, variable lighting, and sediment resuspension. By correcting for detection errors, we estimated that approximately 7300 individuals of Pseudorasbora parva and 80 individuals of Cyprinus carpio passed through the observation site during a seven-hour monitoring period. These findings demonstrate the feasibility of deep learning-based monitoring to capture temporal patterns of fish occurrence in agricultural drainage canals. This approach provides a promising tool for sustainable aquatic ecosystem management in agricultural landscapes and emphasizes the need for further improvements in recall under turbid and low-visibility conditions. Full article

In this study, a hybrid optoelectronic synthetic aperture radar (SAR) moving target detection and imaging (OCMTI) method is introduced to address the challenges faced when processing large volumes of SAR data while focusing on key moving targets. Traditional algorithms often demand substantial computational resources, with the Fourier transform representing a widely implemented yet computationally intensive operation (typically O(N²) or O(NlogN) complexity). In contrast, optical systems can perform Fourier transforms inherently at the speed of light. The OCMTI method leverages this advantage and integrates optical and electronic processing to enable the rapid detection and selective imaging of moving targets. First, imaging parameters are dynamically configured based on the velocity range of the moving targets of interest and multiple coarse images of the entire scene are generated using an optical system. These images are then processed using a computer-aided detection system to identify candidate targets, and each target is subjected to fine imaging and parameter estimation. The refined images of detected targets are finally integrated into a single image with a suppressed background. The OCMTI method can rapidly detect moving targets, and the time complexity of moving target detection is proportional to the number of image pixels. The correct detection rate for a single image can reach 97%. The efficiency of this method in detecting and imaging moving targets is experimentally validated, which reveals it as a promising solution for time-sensitive applications. The OCMTI method bridges optical speed with electronic flexibility, thereby advancing SAR systems toward real-time, target-oriented operations. Full article

Infrared-visible image fusion quality is critical for nighttime perception in autonomous driving and surveillance but suffers severe degradation under extreme low-light conditions, including irreversible texture loss in visible images, thermal boundary diffusion artifacts, and overexposure under dynamic non-uniform illumination. To address these challenges, a Decomposition–Disentanglement–Dynamic Compensation framework, D3Fusion, is proposed. Firstly, a Retinex-inspired Decomposition Illumination Net (DIN) decomposes inputs into enhanced images and degradative illumination maps for joint low-light recovery. Secondly, an illumination-guided encoder and a multi-scale differential compensation decoder dynamically balance cross-modal features. Finally, a progressive three-stage training paradigm from illumination correction through feature disentanglement to adaptive fusion resolves optimization conflicts. Compared to State-of-the-Art methods, on the LLVIP, TNO, MSRS, and RoadScene datasets, D3Fusion achieves an average improvement of 1.59% in standard deviation (SD), 6.9% in spatial frequency (SF), 2.59% in edge intensity (EI), and 1.99% in visual information fidelity (VIF), demonstrating superior performance in extreme low-light scenarios. The framework effectively suppresses thermal diffusion artifacts while mitigating exposure imbalance, adaptively brightening scenes while preserving texture details in shadowed regions. This significantly improves fusion quality for nighttime images by enhancing salient information, establishing a robust solution for multimodal perception under illumination-critical conditions. Full article

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions. Full article

Accurately simulating the asymmetric spectral profiles of blue LEDs is crucial for photobiological research, yet it remains a challenge for traditional symmetric models. This study proposes a novel spectral simulation model that effectively captures these asymmetries. The proposed model structure is partly motivated by known broadening and dispersion mechanisms observed in real LED spectra; it employs a ‘base model + correction’ framework, where an Exponentially Modified Gaussian (EMG) function captures the primary spectral shape and falling edge and an Asymmetric Pseudo-Voigt (APV) function corrects the deviations on the rising edge. Requiring only the central wavelength and bandwidth as user inputs, the simulation results exhibit a high degree of agreement with the experimental data spectra. The model provides a rapid and robust tool for pre-evaluating light sources against regulatory criteria (e.g., >99% of the spectral intensity is in the 400–500 nm band), thereby enhancing the efficiency of experimental design in blue light protection studies. Full article