Search Results (516)

Depth profiling time of flight secondary ion mass spectrometry (TOF-SIMS) enables imaging the distributions of unlabeled metabolites within cells. When depth profiling TOF-SIMS is performed on intact cells, the 3D renderings produced by stacking and rending the individual depth profiling images are distorted along the z-axis, which complicates image interpretation. Here we describe an approach for correcting the z-axis distortion in 3D TOF-SIMS depth profiling images of cells. This approach uses the total ion images collected during TOF-SIMS depth profiling to create a 3D morphology model of the cell’s surface at the time when each depth profiling image was acquired. These morphology models are used to correct the z-position and height of each voxel in the component-specific 3D TOF-SIMS images. We have applied this approach to 3D TOF-SIMS depth profiling images that show endoplasmic reticulum-plasma membrane (ER-PM) junctions in cells that are a simplified model of ER-PM junctions in neuronal cells. The depth corrected 3D image more accurately depicted the structure of the ER-PM junctions than the uncorrected image. Projection of the depth corrected 3D image on the model of the cell’s morphology facilitated visualization of the ER-PM junctions relative to the peaks, ridges and valleys on the surface of the cell. Thus, accurate component-specific 3D images may now be produced for depth profiling TOF-SIMS datasets. This approach may facilitate efforts to identify the lipids and other metabolites that reside in ER-PM junctions in neuronal cells and elucidate their roles in neuronal function. Full article

Marine benthic organism detection (BOD) is essential for underwater robotics and seabed resource management but suffers from motion blur, perspective distortion, and background clutter in dynamic underwater environments. To address visual feature degradation and computational constraints, we, in this paper, introduce EMR-YOLO, a deep learning based multi-scale BOD method. To handle the diverse sizes and morphologies of benthic organisms, we propose an Efficient Detection Sparse Head (EDSHead), which combines a unified attention mechanism and dynamic sparse operators to enhance spatial modeling. For robust feature extraction under resource limitations, we design a lightweight Multi-Branch Fusion Downsampling (MBFDown) module that utilizes cross-stage feature fusion and multi-branch architecture to capture rich gradient information. Additionally, a Regional Two-Level Routing Attention (RTRA) mechanism is developed to mitigate background noise and sharpen focus on target regions. The experimental results demonstrate that EMR-YOLO achieves improvements of 2.33%, 1.50%, and 4.12% in AP, AP₅₀, and AP₇₅, respectively, outperforming state-of-the-art methods while maintaining efficiency. Full article

Video stabilization is a critical technology for enhancing visual content quality in dynamic shooting scenarios, especially with the widespread adoption of mobile photography devices and Unmanned Aerial Vehicle (UAV) platforms. While traditional digital stabilization algorithms can improve frame stability by modeling global motion trajectories, they often suffer from excessive cropping or boundary distortion, leading to a significant loss of valid image regions. To address this persistent challenge, we propose the View Out-boundary Synthesis Algorithm (VOSA), a symmetry-aware spatio-temporal consistency framework. By leveraging rotational and translational symmetry principles in motion dynamics, VOSA realizes optical flow field extrapolation through an encoder–decoder architecture and an iterative boundary extension strategy. Experimental results demonstrate that VOSA enhances conventional stabilization by increasing content retention by 6.3% while maintaining a 0.943 distortion score, outperforming mainstream methods in dynamic environments. The symmetry-informed design resolves stability–content conflicts and outperforms mainstream methods in dynamic environments, establishing a new paradigm for full-frame stabilization. Full article

Computer vision-based part surface inspection is widely used for quality evaluation. However, challenges such as low image quality, caused by factors like inadequate acquisition equipment, camera vibrations, and environmental conditions, often lead to reduced detection accuracy. Although super-resolution reconstruction can enhance image quality, existing methods face issues such as limited accuracy, information distortion, and high computational cost. To overcome these challenges, we propose a novel super-resolution reconstruction method for part images that incorporates adaptive multi-scale object tracking. Our approach first adaptively segments the input sequence of part images into blocks of varying scales, improving both reconstruction accuracy and computational efficiency. Optical flow is then applied to estimate the motion parameters between sequence images, followed by the construction of a feature tracking and sampling model to extract detailed features from all images, addressing information distortion caused by pixel misalignment. Finally, a non-linear reconstruction algorithm is employed to generate the high-resolution target image. Experimental results demonstrate that our method achieves superior performance in terms of both quantitative metrics and visual quality, outperforming existing methods. This contributes to a significant improvement in subsequent part detection accuracy and production efficiency. Full article

The visual cryptography scheme (VCS) is a fundamental image encryption technique that divides a secret image into two or more shares, such that the original image can be revealed by superimposing a sufficient number of shares. A major limitation of conventional VCS methods is pixel expansion, wherein the generated shares and reconstructed image are typically at least twice the size of the original. Additionally, thin lines or curves—only one pixel wide in the original image—often appear distorted or duplicated in the reconstructed version, a distortion known as the thin-line problem (TLP). To eliminate the reliance on predefined codebooks inherent in traditional VCS, Kafri introduced the Random Grid visual cryptography scheme (RG-VCS), which eliminates the need for such codebooks. This paper introduces novel algorithms that are among the first to explicitly address the thin-line problem in the context of random grid based schemes. This paper presents novel visual cryptography algorithms specifically designed to address the thin-line preservation problem (TLP), which existing methods typically overlook. A comprehensive visual and numerical comparison was conducted against existing algorithms that do not explicitly handle the TLP. The proposed methods introduce adaptive encoding strategies that preserve fine image details, fully resolving TLP-2 and TLP-3 and partially addressing TLP-1. Experimental results show an average improvement of 18% in SSIM and 13% in contrast over existing approaches. Statistical t-tests confirm the significance of these enhancements, demonstrating the effectiveness and superiority of the proposed algorithms. Full article

Multi-layer multi-pass welding plays an important role in manufacturing industries such as nuclear power plants, pressure vessel manufacturing, and ship building. However, distortion or welding defects are still challenges; therefore, welding monitoring and quality control are essential tasks for the dynamic adjustment of execution during welding. The aim was to propose a machine vision system for monitoring and surface quality evaluation during multi-pass welding using a line scanner and infrared camera sensors. The cross-section modelling based on the line scanner data enabled the measurement of distortion and dynamic control of the welding plan. Lack of fusion, porosity, and burn-through defects were intentionally generated by controlling welding parameters to construct a defect inspection dataset. To reduce the influence of material surface colour, the proposed normal map approach combined with a deep learning approach was applied for inspecting the surface defects on each layer, achieving a mean average precision of 0.88. In addition to monitoring the temperature of the weld pool, a burn-through defect detection algorithm was introduced to track welding status. The whole system was integrated into a graphical user interface to visualize the welding progress. This work provides a solid foundation for monitoring and potential for the further development of the automatic adaptive welding system in multi-layer multi-pass welding. Full article

This article presents a novel method for detecting series arc faults (SAFs) in residential applications using time-domain imaging (TDI) and Long Short-Term Memory (LSTM) networks. The proposed method transforms current signals into grayscale images by filtering out the fundamental frequency, allowing key arc fault characteristics—such as high-frequency noise and waveform distortions—to become visually apparent. The use of Ensemble Empirical Mode Decomposition (EEMD) helped isolate meaningful signal components, although it was computationally intensive. To address real-time requirements, a simpler yet effective TDI method was developed for generating 2D images from current data. These images were then used as inputs to an LSTM network, which captures temporal dependencies and classifies both arc faults and appliance types. The proposed TDI-LSTM model was trained and tested on 7000 labeled datasets across five common household appliances. The experimental results show an average detection accuracy of 98.1%, with reduced accuracy for loads using thyristors (e.g., dimmers). The method is robust across different appliance types and conditions; comparisons with prior methods indicate that the proposed TDI-LSTM approach offers superior accuracy and broader applicability. Trade-offs in sampling rates and hardware implementation were discussed to balance accuracy and system cost. Overall, the TDI-LSTM approach offers a highly accurate, efficient, and scalable solution for series arc fault detection in smart home systems. Full article

As part of contemporary urban culture, murals support place making and city identity. While much attention has been paid to their role in activating public space during daylight hours, their presence after dark remains largely unexamined. This paper analyzes how mural content interacts with night-time illumination. The research draws on case studies, photographs, luminance measurements, and lighting simulations. It evaluates how existing lighting systems support or undermine the legibility and impact of commercial murals in urban environments. It explores whether standardized architectural lighting guidelines suit murals, how color and surface affect visibility, and which practices improve night-time legibility. The study identifies a gap in existing lighting strategies, noting that uneven lighting distorts intent and reduces public engagement. In response, a new design tool—the Floodlighting Content Readability Map—is proposed to support artists and planners in creating night-visible murals. This paper situates mural illumination within broader debates on creative urbanism and argues that lighting is not just infrastructure, but a cultural and aesthetic tool that extends the reach and resonance of public art in the 24 h city. It further emphasizes the need for interdisciplinary collaboration and a multi-contextual perspective—encompassing visual, social, environmental, and regulatory dimensions—when designing murals in cities. Full article

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article

Various parameters and observation conditions contribute to the emergence of color. This phenomenon poses a challenge in modern visual communication systems, which are continuously being enhanced through new insights gained from research into specific psychophysical effects. One such effect is the psychophysical phenomenon of simultaneous contrast. Nearly 90 years ago, Kurt Koffka described one of the earliest illusions related to simultaneous contrast. This study examined the perception of gray tone variations in the Koffka ring against different background color combinations (red, blue, green) displayed on a computer screen. The intensity of the effect was measured using lightness difference ΔL₀₀ across light-, medium-, and dark-gray tones. The results were analyzed using descriptive statistics, while statistically significant differences were determined using the Friedman ANOVA and post hoc Wilcox tests. The strongest visual effect was observed the for dark-gray tones of the Koffka ring on blue/green and red/green backgrounds, indicating that perceptual organization and spatial parameters influence the illusion’s magnitude. The findings suggest important implications for digital media design, where understanding these effects can help avoid unintended color tone distortions caused by simultaneous contrast. Full article

Stereoscopic omnidirectional images (SOIs) have gained significant attention for their immersive viewing experience by providing binocular depth with panoramic scenes. However, evaluating their visual quality remains challenging due to its unique spherical geometry, binocular disparity, and viewing conditions. To address these challenges, this paper proposes a dual-branch deep learning framework that integrates spherical structural features and perceptual binocular cues to assess the quality of SOIs without reference. Specifically, the global branch leverages spherical convolutions to capture wide-range spatial distortions, while the local branch utilizes a binocular difference module based on discrete wavelet transform to extract depth-aware perceptual information. A feature complementarity module is introduced to fuse global and local representations for final quality prediction. Experimental evaluations on two public SOIQA datasets—NBU-SOID and SOLID—demonstrate that the proposed method achieves state-of-the-art performance, with PLCC/SROCC values of 0.926/0.918 and 0.918/0.891, respectively. These results validate the effectiveness and robustness of our approach in stereoscopic omnidirectional image quality assessment tasks. Full article

Transparent polyimides with excellent mechanical properties and high optical transmittance have been widely used in various optical and electrical applications. However, due to the rigidity of their aromatic structure, their flexibility is limited, making them unsuitable for applications requiring different form factors, such as flexible display cover windows. Furthermore, the refractive index of most transparent polyimides is approximately 1.57, which differs from that of the optically clear adhesives (OCAs) and window materials that have values typically around 1.5, resulting in visual distortion. This study employed 4,4′-(hexafluoroisopropylidene)diphthalic anhydride (6FDA) and 2,2′-bis(trifluoromethyl)benzidine (TFMB) as the base structure of polyimides (6T). Additionally, 1,3-bis(aminomethyl)cyclohexane (BAC) with a monocyclic structure and bis(aminomethyl)bicyclo[2,2,1]heptane (BBH) with a bicyclic structure were introduced as co-monomers or crosslinking agents to 6T. The mechanical, thermal, and optical properties of the obtained copolymers (6T-BAC and 6T-BBH series) and crosslinked polymers (6T-CL-BAC and 6T-CL-BBH series) were compared. Both the copolymer series (6T-BAC and 6T-BBH) and the crosslinked series (6T-CL-BAC and 6T-CL-BBH) exhibited improved optical properties compared to the conventional 6T, with maximum transmittance exceeding 90% and refractive indices ranging from approximately 1.53 to 1.55. Notably, the copolymer series achieved transmittance levels above 95% and exhibited lower refractive indices (~1.53), demonstrating superior optical performance relative not only to the 6T baseline but also to the crosslinked series. The alicyclic polyimides synthesized in this study exhibited mechanical flexibility, high optical transmittance, and a refractive index approaching 1.5, demonstrating their applicability for use as flexible display cover window materials. Full article

Regarding the distortion correction problem of large field of view wide-angle cameras commonly used in railway visual inspection systems, this paper proposes a novel online calibration method for non-specially made cooperative calibration objects. Based on the radial distortion divisor model, first, the spatial coordinates of natural spatial landmark points are constructed according to the known track gauge value between two parallel rails and the spacing value between sleepers. By using the image coordinate relationships corresponding to these spatial coordinates, the coordinates of the distortion center point are solved according to the radial distortion fundamental matrix. Then, a constraint equation is constructed based on the collinear constraint of vanishing points in railway images, and the Levenberg–Marquardt algorithm is used to found the radial distortion coefficients. Moreover, the distortion coefficients and the coordinates of the distortion center are re-optimized according to the least squares method (LSM) between points and the fitted straight line. Finally, based on the above, the distortion correction is carried out for the distorted railway images captured by the camera. The experimental results show that the above method can efficiently and accurately perform online distortion correction for large field of view wide-angle cameras used in railway inspection without the participation of specially made cooperative calibration objects. The whole method is simple and easy to implement, with high correction accuracy, and is suitable for the rapid distortion correction of camera images in railway online visual inspection. Full article

Existing Simultaneous Localization and Mapping (SLAM) algorithms provide precise local pose estimation and real-time scene reconstruction, widely applied in autonomous navigation for land vehicles. However, the odometry of SLAM algorithms exhibits localization drift and error divergence over long-distance operations due to the lack of inherent global constraints. In this paper, we propose a real-time geo-localization method for land vehicles, which only relies on a LiDAR-inertial-visual SLAM (LIV-SLAM) and a referenced image. The proposed method enables long-distance navigation without requiring GPS or loop closure, while eliminating accumulated localization errors. To achieve this, the local map constructed by SLAM is real-timely projected onto a downward-view image, and a highly efficient cross modal matching algorithm is proposed to estimate the global position by aligning the projected local image to a geo-referenced satellite image. The cross-modal algorithm leverages dense texture orientation features, ensuring robustness against cross-modal distortion and local scene changes, and supports efficient correlation in the frequency domain for real-time performance. We also propose a novel adaptive Kalman filter (AKF) to integrate the global position provided by the cross-modal matching and the pose estimated by LIV-SLAM. The proposed AKF is designed to effectively handle observation delays and asynchronous updates while simultaneously rejecting the impact of erroneous matches through an Observation-Aware Gain Scaling (OAGS) mechanism. We verify the proposed algorithm through R³LIVE and NCLT datasets, demonstrating superior computational efficiency, reliability, and accuracy compared to existing methods. Full article