Search Results (2,303)

Infrared thermography is a non-contact tool for monitoring inflammatory processes in the diabetic foot, but quantitative bedside use remains challenging with low-cost thermal infrared cameras due to radiometric drift, non-uniformity (vignetting), geometric distortions, and visible–thermal parallax. This paper presents an end-to-end clinical and instrumental framework built around a cheap thermal camera to ensure reproducible acquisition and physically consistent temperature estimation. The approach combines a standardized mobile acquisition setup and measurement protocol, extraction of embedded radiometric data from raw images, radiometric inversion with atmospheric correction, vignette correction performed in the radiometric domain, and geometric calibration of both visible and infrared sensors using dedicated (thermal) calibration targets. Accurate visible–infrared registration is obtained from hybrid heated markers, enabling reliable overlay and downstream analysis. The full processing chain yields quantitative thermograms with radiometric errors below 0.15 °C and sub-pixel multimodal alignment, supporting the detection of clinically relevant plantar temperature asymmetries and paving the way for routine calibrated low-cost thermography in diabetic foot care. Full article

Carbon Fiber Reinforced Polymers (CFRP) are extensively utilized in high-performance engineering, yet localized structural discontinuities can severely compromise their integrity. This paper aims to achieve high-sensitivity characterization of such anomalies using a proposed acoustic shearography technique based on continuous acoustic excitation. A comprehensive finite element model (FEM) was developed to clarify the mechanical-energy coupling between the acoustic fields and localized surface strain field modulations. By exploiting ultrasonic energy coupling, the localized features of discontinuities were identified through full-field, non-contact optical measurement of localized phase distortions. Key parameters, including shearing amount, excitation frequency, driving voltage, and geometric characteristics of blind flat-bottom holes (BFBH), were systematically investigated. The results demonstrate a high correlation between FEM simulations and experimental observations quantitatively elucidating how defect diameter and hole depth modulate surface strain distributions. The proposed hybrid acoustic optical approach achieves near-instantaneous full field imaging within a millisecond timeframe typically under 200 ms. Additionally, the methodology leverages localized acoustic resonance to significantly boost the signal-to-noise ratio (SNR) resulting in highly quantified phase map contrast. Full article

Low-orbit thermal infrared bidirectional whisk-broom imaging offers wide-swath coverage and high spatial resolution for monitoring moving targets such as aircraft, but large scan angles and terrain undulation cause non-rigid geometric distortion and radiometric inconsistency between forward and backward scans. These effects generate strong clutter in difference images and degrade small and weak target detection. To address this problem, we propose an infrared small target detection method that fuses accurate registration and weighted difference. First, we propose a hybrid multi-scale registration algorithm that achieves coarse affine registration through sparse feature–point matching and then iteratively corrects nonlinear deformations by integrating a global grayscale-driven force with a local sparse-feature-guided force, yielding a registration error of 0.3281 pixels. On this basis, a multi-scale weighted convolutional morphological difference algorithm is proposed. A novel dual-structure hollow top-hat transform is constructed to accurately estimate the background, and a multi-directional convolution mechanism is introduced to effectively suppress anisotropic edge clutter and enhance target saliency. Experiments on SDGSAT-1 thermal infrared bidirectional whisk-broom data show an SCRG of 18.27, and a detection rate of 91.2% when the false alarm rate is below 0.15%. The method outperforms representative competing algorithms and provides a useful reference for space-based aerial moving target detection. Full article

Background/Objectives: To evaluate and compare the qualitative and quantitative image performance of multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) against conventional single-shot (ss-DWI) and high-resolution single-shot (HR-ssDWI) sequences in patients with esophageal cancer. Methods: Twenty patients who underwent esophagus MRI, including ss-DWI, HR-ssDWI and MUSE-DWI, were retrospectively enrolled. Image quality, esophageal contour, lesion conspicuity and image distortion were independently graded by two radiologists using a five-point scale and compared between the three sequences. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of esophageal tissue were measured and compared between the three sequences. Results: After Bonferroni correction (p < 0.017), MUSE-DWI had significantly higher scores than HR-ssDWI in image quality, esophageal contour delineation and lesion conspicuity, and all three sequences had statistically significant differences in image distortion scores with MUSE-DWI performing the best. Quantitative analysis revealed that MUSE-DWI had the highest SNR and CNR values; significant differences were found in SNR between ss-DWI and HR-ssDWI (p < 0.001), and in both SNR and CNR between HR-ssDWI and MUSE-DWI (p < 0.001), while no significant differences were observed in SNR and CNR between ss-DWI and MUSE-DWI (p > 0.017). Conclusions: MUSE-DWI outperforms ss-DWI and HR-ssDWI in reducing image distortion, with comparable quantitative image quality metrics to ss-DWI. It represents a valuable optimized DWI technique for esophageal clinical imaging. Full article

Vascular intervention has advanced technically faster than it has matured methodologically. Across neurovascular, carotid, peripheral, and aortic practice, complications and outcomes are often reported using different definitions, thresholds, surveillance strategies, adjudication methods, follow-up schedules, and units of analysis. As a result, studies that appear to assess the same treatment may in fact be measuring different outcome constructs. This problem is particularly visible in neurovascular intervention, where technical, radiographic, and clinical outcomes are often combined within the same evaluative framework. In acute ischemic stroke thrombectomy, changes in reperfusion thresholds can alter the meaning of procedural success. In intracranial aneurysm treatment, angiographic occlusion, retreatment, delayed stenosis, and neurological morbidity are often reported together despite representing different dimensions of efficacy and safety, while the interpretation of surrogate angiographic outcomes may vary across device classes. Similar issues arise in carotid intervention, peripheral endovascular therapy, and endovascular aneurysm repair, where composite outcomes, imaging-detected complications, and inconsistent surveillance protocols further complicate interpretation. These variations limit cross-study comparability, weaken meta-analytic synthesis, and may distort judgments about treatment effectiveness and safety. Endpoint heterogeneity persists partly through disciplinary silos, device-driven evaluation frameworks, and regulatory pathways that favor surrogate over clinical endpoints; addressing it will require not only better reporting but standardized outcome constructs, coordinated international registries, and broader adoption of core outcome set methodology. Greater discipline in endpoint definition and reporting, together with broader adoption of standardized outcome frameworks and core outcome set methodology, is needed if evidence in vascular intervention is to accumulate coherently. Full article

Single image dehazing remains challenging because haze simultaneously distorts global illumination, scene structure, and fine textures, making rigid low–high frequency decoupling prone to error propagation and detail inconsistency. To address this issue, we propose CoFiWaveMamba, a coarse-to-fine wavelet-guided Mamba network for single image dehazing. The proposed method first employs wavelet decomposition to separate low- and high-frequency components. For low-frequency restoration, a 2D selective-scan Mamba-based module is introduced to capture long-range dependencies, combined with lightweight high-frequency-guided spatial modulation and Shuffle-guided Sequence Attention, we design a progressive coarse-to-fine refinement strategy that combines Fourier-domain global spectral consistency with wavelet-domain directional detail representation, enabling more targeted recovery of edges and textures. Experiments on synthetic and real dehazing benchmarks, including Haze4K, RESIDE-6K, HSTS-SYNTHETIC, I-Haze, NH-Haze, Dense-Haze, and O-HAZE, as well as ablation studies, verify the effectiveness of the proposed design. Overall, CoFiWaveMamba provides a more coordinated solution for global haze removal and local detail reconstruction, helping suppress residual haze, ringing artifacts, oversharpening, and texture inconsistency while restoring clearer and more natural images. Full article

Wide-swath longwave infrared (LWIR) imaging from Low Earth Orbit (LEO) demands fast optics and rectilinear (F-tan) mapping for thermal mapping and multi-frame registration. Achieving an F/1.2 aperture with a 112° diagonal field of view (FOV) and distortion within ±5% is challenging, as mapping constraints and field-dominant off-axis aberrations become strongly coupled at large chief-ray angles. The low-distortion target is not only a geometric specification, but also a practical requirement that reduces peripheral compression, helps maintain edge-detail consistency, and lowers digital de-warping effort in the processing pipeline. While traditional LWIR secondary imaging is predominantly restricted to narrow-field cooled systems for cold-stop constraints, the proposed architecture utilizes a curved intermediate image to effectively decouple mapping formation in the field-dominant front objective from aperture-dominant correction in the rear group. Using chalcogenide glasses, the lens achieves a 5.7 mm effective focal length within a 186.9 mm total track. Analysis over the 8–12 μm band confirms performance approaching the diffraction limit at the 50 lp/mm Nyquist frequency alongside stable geometric fidelity across the full field. Thermal analysis from −40 °C to 80 °C and Monte Carlo tolerance analysis demonstrate stable imaging performance and manufacturing feasibility, confirming the effectiveness of the proposed design approach. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

As AI-driven educational systems increasingly rely on deep neural networks, their vulnerability to adversarial perturbations raises concerns about assessment integrity, fairness, and reliability. Adversarial example purification is attractive for such deployments because it removes input perturbations without modifying the already deployed models. However, most existing purification methods are inherently goal-free: denoising-based approaches apply blind heuristic operators, while reconstruction-based methods rely on stochastic sampling guided by natural image priors. These methods typically suppress perturbations at the cost of weakening semantic details or inducing structural distortions. To address this limitation, we propose a novel goal-directed purification framework, termed adversarial energy field optimization for adversarial example purification (AEFOP). AEFOP formulates purification as a constrained optimization problem by defining a learnable adversarial energy which quantifies how far an input deviates from the benign region. This allows adversarial examples to be explicitly pushed from high-energy regions toward low-energy benign regions along an interpretable descent trajectory. Specifically, we build an adversarial energy network and optimize the energy field via a two-stage strategy: adversarial energy field shaping, which enforces distance-like energy behavior and correct gradient directions, and task-driven energy field calibration, which unrolls the descent process to calibrate the field with classification-consistency and semantic-preservation objectives. Extensive experiments across multiple attack scenarios demonstrate that AEFOP achieves superior purification accuracy and high visual quality while requiring only a few gradient steps during inference, offering a practical and efficient robustness layer for vision-based AI services in education. Full article

Vision-based pose estimation has been widely applied in unmanned aerial vehicle (UAV) navigation. However, existing visual pose estimation algorithms are highly sensitive to camera imaging distortion, which degrades estimation accuracy, and often suffer from noticeable jitter between frames in dynamic scenarios. To address these issues, this paper proposes an improved visual pose estimation algorithm built upon the Perspective Similar Triangle (PST) geometric model. Using a planar fiducial marker as the observation target, the single-frame pose estimation problem is reformulated as a hierarchical geometric inference framework, including image point distortion correction, depth recovery based on planar similar triangle constraint, and rigid transformation estimation between the camera and world coordinate systems. This formulation improves pose estimation accuracy under distorted imaging conditions. To accommodate distortion variations in practical scenarios, a radial distortion coefficient update method is further designed to adaptively adjust the radial distortion parameters under single-frame observations, ensuring that the distortion model remains consistent with the actual imaging distortion and providing reliable model inputs for distortion correction in pose estimation. In addition, to enhance pose stability in dynamic scenarios, a multi-frame optical center consistency constraint (MOCCC) method is introduced to optimize the pose estimation for more stability. By constraining pose estimation across adjacent frames using the mean optical center over multiple frames as the optimization objective, the proposed method effectively suppresses pose jitter caused by single-frame observation noise. Finally, a three-degree-of-freedom (3-DOF) attitude motion platform is established, and both static and dynamic experimental scenarios are designed to validate the accuracy and stability of the proposed algorithm. Experimental results demonstrate that the proposed algorithm achieves high accuracy and high stability pose estimation under imaging distortion and small perturbations, exhibiting good robustness and suitability for practical UAV visual navigation applications. Full article

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article

Egyptian papyri are commonly documented using high-resolution two-dimensional imaging, which enhances legibility but does not adequately capture the micrometric surface morphology required for material and conservation studies. To address this limitation, we developed and validated an integrated, fully non-contact imaging workflow combining Ultra-Close-Range Multiband Photogrammetry with Reflectance Transformation Imaging (RTI) and normal map integration. The protocol was tested on six papyrus fragments from the Museo Egizio di Torino (XXI Dynasty–Byzantine period) exhibiting different conservation conditions. Multiband photogrammetry in the visible and visible-induced infrared luminescence bands achieved a Ground Sample Distance of 17 µm/px and a point cloud density of approximately 170 points/mm², enabling detailed analysis of fiber morphology, surface deformation, and the spatial distribution of Egyptian blue. RTI-based normal map integration provided complementary high-frequency surface information with reduced acquisition and processing times. To overcome RTI low-frequency distortions, a revised normal integration strategy was implemented using surface planarization and frequency-domain fusion with photogrammetric data based on Power Spectral Density analysis. The resulting hybrid models combine metric reliability with enhanced surface detail, providing a scalable and non-invasive approach for papyrological documentation and conservation research. Full article

The automatic generation of semantic Level of Detail (LOD) 2 models from TomoSAR point clouds is frequently compromised by elevation side-lobes, data sparsity, and inherent geometric distortions. In particular, the energy dispersion caused by side-lobes blurs vertical structures, making the extraction of floor details and accurate floor height estimation significantly challenging. To overcome these limitations, we present a refined reconstruction framework that tightly couples tomographic imaging mechanisms with building geometric priors. For fine-grained vertical reconstruction, we employ a geometry-constrained inverse projection strategy that concentrates scattered energy back onto the building façade to mitigate side-lobe interference. This is complemented by a Global Coherent Integration method, utilizing spectral analysis to robustly recover periodic floor patterns and estimate average floor heights. In the horizontal domain, we address the conflict between noise suppression and feature preservation through a separation-of-axes morphological strategy. Unlike traditional isotropic filtering, this approach processes orthogonal directions independently to bridge data gaps while strictly maintaining sharp building corners and recovering fine substructures. Validated on airborne Ku-band datasets, the proposed method demonstrates the capability to produce topologically complete and semantically rich urban models from sparse radar observations. Full article

This paper presents Diff-GTISR, a novel diffusion-based model for achieving super-resolution in thermal images guided by a high-resolution visible image. Thermal sensors are widely used in surveillance, safety, and industrial inspection; however, their limited spatial resolution constrains thermal image quality because of the low resolution. Thermal image super-resolution is thus critical to compensate for this limitation. The increasing prevalence of multisensor platforms has resulted in the availability of high-resolution visible images, providing effective guidance to enhance thermal image resolution. Recently, diffusion-based super-resolution has demonstrated strong capability in recovering perceptually plausible details; however, such models often underperform in distortion-oriented metrics compared with transformer-based approaches. To address this gap, the proposed Diff-GTISR method employs a modality-specific dual encoder to extract multiscale features and a cross-modal guidance attention module to transfer structural information from visible images into low-resolution thermal images. Also, a refinement network is employed to improve the method further. The experimental results indicate that Diff-GTISR consistently enhances perceptual quality in comparison to state-of-the-art diffusion-based methods. Furthermore, it is superior to transformer-based methods in terms of distortion performance. Full article

Multispectral remote sensing image super-resolution (RSISR) aims to reconstruct high-frequency details while preserving cross-band structural consistency under strict computational budgets. However, real-world satellite imagery exhibits heterogeneous distortions, ranging from band-dependent noise to spatially varying texture degradation, rendering uniform restoration strategies suboptimal. To address these challenges, we propose a unified framework that integrates cue extraction, expert specialization, and efficiency-aware restoration. Specifically, a Distortion-Aware Feature Extractor (DAFE) explicitly encodes distortion cues by synthesizing fixed frequency bases, learnable residual components, lightweight spatial edge representations, and noise proxies. Subsequently, a Distortion-Aware Expert Choice (DAEC) router utilizes these cues to establish distortion-conditioned affinities and performs capacity-constrained, load-balanced expert assignment. Finally, a parameter-shared Mixture-of-Experts (PS-MoE) architecture employs shared expert parameters across spectral bands, augmented by band-wise low-rank adapters, to enable coarse-to-fine restoration with minimal computational overhead. Extensive experiments on the SEN2VENμS and OLI2MSI datasets demonstrate that the proposed method achieves a PSNR of 49.38 dB on SEN2VENμS 2×, 45.91 dB on SEN2VENμS 4×, and 45.94 dB on OLI2MSI 3×. Compared to the strongest baseline for each task, our method yields PSNR improvements of 0.12 dB, 0.10 dB, and 0.09 dB, respectively, while simultaneously reducing FLOPs and parameter counts. These results confirm that explicit distortion modeling and parameter-shared expert specialization provide an effective and computationally efficient solution for multispectral remote sensing image super-resolution. Full article