Search Results (17,271)

Background/Objectives: The objective of this study was to ascertain whether photon-counting CT angiography (PCD-CTA) can optimize image quality for the visualization of perforating arteries for planning fibular transplant procedures in comparison to energy-integrating CT angiography (EID-CTA). Methods: In this retrospective single-center study, all patients who underwent preoperative CT of the peripheral runoff for planning between October 2021 and July 2023 were consecutively included. PCD-CTA was performed in standard resolution mode as 55 keV images with 90 mL of iodine-containing contrast agent or alternatively, an EID-CTA as a low-kV scan with 110 mL of contrast agent. The raw data were reformatted using comparable soft vascular and sharp regular convolution kernels, slice thickness/increment, and field of view. Contrast-to-noise ratio was calculated for objective image quality. Subjective evaluation was based on a rating by three radiologists using a five-point Likert scale (criteria: overall image quality, luminal attenuation, vessel sharpness, and perforator depiction). Results: Of the 26 patients who were screened, 9 could be included in each group, while 8 were excluded due to incomplete reconstructions. The reduction in contrast agent dose resulted in a non-significant decrease in luminal attenuation on PCD-CTA (452.5 ± 53.6 HU vs. 465.5 ± 99.6 HU; p = 0.375). The image noise was considerably lower for PCD-CTA (21.1 ± 1.0 HU vs. 32.9 ± 1.6 HU; p < 0.001). This resulted in a significantly higher contrast-to-noise ratio (CNR) for sharp kernel reconstructions (22.4 ± 3.5 vs. 14.5 ± 3.8; p < 0.001). No significant differences were observed for the soft vascular kernel. Subjective evaluation revealed a significant enhancement in overall image quality, vascular sharpness, and perforator depiction for PCD-CTA with sharp reconstructions. In contrast, soft kernel reconstructions and luminal attenuation demonstrated no substantial difference. Interrater agreement was good to excellent. Conclusions: PCD-CTA with sharp kernel reformatting has been demonstrated to yield superior image quality and perforator delineation of the fibular artery in comparison to standard EID-CTA. Full article

Background/Objectives: The presence of untreated second mesio-buccal canals (MB2) in maxillary first molars is usually associated with endodontic treatment failure. Previous CBCT-based investigations have evaluated the quality of root canal fillings and the prevalence of apical lesions in endodontically treated teeth. However, evidence specifically addressing untreated MB2 canals and their association with apical periodontitis remains limited. Therefore, the aim of this cross-sectional study was to evaluate the prevalence of unfilled MB2 canals in endodontically treated maxillary first molars and their association with apical periodontitis. Methods: CBCT scans of 75 patients from an endodontic practice were retrospectively analyzed. Maxillary first molars (teeth 16 and 26) were evaluated for the presence and filling status of root canals (MB1, MB2, palatal, distal) and the presence of periapical radiolucency using the CBCT periapical index. Two calibrated examiners independently assessed all images. The association between unfilled MB2 canals and apical periodontitis was analyzed using chi-square tests, and odds ratios with 95% confidence intervals were calculated. Results: The mean patient age was 53.4 ± 15.5 years (range: 14–80). An MB2 canal was present in 84% (63/75) of eligible teeth. Among teeth with an MB2 canal, only 20.6% (13/63) were endodontically filled, while 79.4% remained untreated. Apical periodontitis was observed in 65.3% (49/75) of all teeth. A significant association was found between unfilled MB2 canals and apical periodontitis (p < 0.001), with an odds ratio of 0.095 (95% CI: 0.022–0.402), indicating that filled MB2 canals significantly reduced the possible risk of periapical pathology. Conclusions: A high prevalence of unfilled MB2 canals was observed in this German population (79.4%). Furthermore, unfilled MB2 canals were strongly associated with apical periodontitis. Therefore, clinicians should utilize all available diagnostic tools, including CBCT and dental microscopes, to maximize MB2 canal identification and improve endodontic treatment outcomes. Full article

Injection-related errors remain a common clinical issue and can cause patient discomfort, hematoma formation, and procedural inefficiencies. The visualization of subcutaneous veins using near-infrared (NIR) imaging has gained attention as an effective approach to reducing such errors, as blood exhibits a higher absorption of NIR light than surrounding tissue. In this study, a low-cost, non-invasive vein visualization system is presented to support safer and more accurate venous access. The proposed system integrates an NIR illumination source and a modified webcam within a compact equipment enclosure, allowing subjects to be conveniently examined by placing their arm inside the device. Vein images are automatically acquired using a laptop-based platform, followed by digital image processing techniques for vein enhancement and visualization. Laboratory-scale experiments were conducted on healthy volunteers to evaluate system performance under multiple conditions, including different vein locations (upper and lower arm regions), varying distances between the NIR light source and the arm (15 cm and 20 cm), and ambient illumination interference (light sources on and off). The experimental results demonstrate the successful implementation and reliable operation of the proposed system. Effective vein visualization was achieved across all test conditions, as confirmed by qualitative visual assessment and quantitative image quality metrics, including the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). Overall, the proposed system offers a practical, accessible, and cost-effective solution for vein visualization, showing strong potential for clinical and experimental applications aimed at reducing injection errors and improving venous access reliability. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at low SNR. To address these issues, this paper proposes a novel robust imaging framework. The framework is built upon two key innovations: a partitioned block-wise compensation mechanism integrated with PSO for simultaneous and precise motion parameters estimation of multiple targets, which avoids local optima and error accumulation; and the application of Sequential Variational Mode Decomposition (SVMD) to adaptively separate and reconstruct signals, thereby suppressing inter-target aliasing and noise interference overlooked in prior studies. Simulations and measured-data experiments confirm that the proposed method maintains clear focusing and superior image quality even at low SNR, outperforming existing techniques in terms of image entropy, contrast, and resolution. This paper provides a robust and effective solution for high-resolution radar surveillance in complex multi-target scenarios. Full article

Hami melon is a widely consumed fruit worldwide, and its sweetness, characterized by soluble solids content (SSC), is a key indicator of fruit quality and commercial value. In this study, hyperspectral imaging combined with machine learning was systematically applied to develop non-destructive models for SSC prediction in multiple Hami melon varieties. Four varieties, namely ‘Xizhoumi’, ‘Jiashigua’, ‘Jinfenghuang’, and ‘Heimeimao’, with a total of 160 samples, were used as the test materials. Hyperspectral images were collected, and SSC was measured at two pulp positions for each sample (denoted as BRIX1 and BRIX2). After applying preprocessing methods including Standard Normal Variate (SNV) transformation and Savitzky–Golay smoothing, five machine learning models were compared: XGBoost, LightGBM, Random Forest (RF), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR). Furthermore, an ensemble modeling strategy based on residual predictive deviation (RPD) weighting from the validation set was proposed. The results show that all models could effectively predict SSC, with the ensemble model achieving the best performance: the coefficients of determination (R²) for BRIX1 and BRIX2 were 0.848 and 0.833, the root mean square errors (RMSEs) were 0.992 and 0.899, the Mean Absolute Percentage Errors (MAPEs) were 6.90% and 6.76%, and the RPD values were 2.57 and 2.45, respectively, demonstrating its strong quantitative analysis capability. This performance benefited from three core optimized designs adopted in this study: (1) a multi-cultivar experimental design that verified the stable correlation between sugar-related spectral features and internal SSC across different Hami melon varieties; (2) an RPD-weighted ensemble modeling strategy that balanced the fitting ability and generalization performance of linear and nonlinear models; and (3) a dual-position SSC measurement design that validated the robustness of the model for SSC prediction at different spatial positions in the pulp. This study confirms the feasibility of hyperspectral imaging technology for non-destructive SSC detection in the four tested Hami melon varieties under laboratory-controlled conditions. The proposed ensemble model achieved a marginal but stable improvement in overall prediction accuracy across the tested varieties compared with the optimal single model, providing a preliminary methodological reference and data support for the development of cross-cultivar non-destructive SSC detection models for Hami melon. Full article

Accurate alfalfa biomass prediction is crucial for pasture management and sustainable livestock production. However, traditional methods often perform poorly under complex field conditions. To address the limited prediction accuracy of traditional methods under complex planting environments, this study proposes an alfalfa biomass prediction method combining multispectral and LiDAR data with ensemble learning model. Based on the multispectral images acquired by unmanned aerial vehicle (UAV) and airborne LiDAR data, the spectral features, three-dimensional structural features, and their interaction features are systematically extracted at the quadrat scale, and a high-quality modeling dataset is constructed by feature selection. Secondly, an ensemble model for alfalfa biomass prediction was constructed, which was composed of random forest, extra trees, and histogram gradient boosting. After model training, the coefficient of determination (R²) of the integrated model on the test set reached 0.813, and the root mean square error (RMSE) and mean absolute error (MAE) were 0.178 kg m⁻² and 0.146 kg m⁻², which were significantly better than those of similar single models. Under feature combinations, the fusion model was better than that of spectral indices only (R² = 0.773) and LiDAR traits only (R² = 0.576), and the model achieved the highest accuracy from bud emergence to early flowering (R² = 0.917). The overall prediction error of the model was approximately normal distribution, and the absolute error of more than 65% of the samples was less than 0.2. However, there was still a trend of underestimation in the high biomass interval. This research showed that the multimodal data fusion and ensemble learning method could achieve high-precision prediction of alfalfa biomass, which provided reliable technical support for pasture resources monitoring and precision agriculture management. Full article

Automotive vision systems depend critically on front-view cameras, whose image quality frequently degrades under adverse conditions such as rain, fog, low illumination, and rapid motion. To address this challenge, we propose VMESR, a variable mamba-enhanced super-resolution network that integrates a selective state-space model into a lightweight super-resolution architecture. By serializing 2D feature maps and applying variable-depth mamba blocks, VMESR captures long-range dependencies with linear complexity. A multi-scale feature extractor, enhanced residual modules equipped with a convolutional block attention module, and dense fusion connections work together to improve the recovery of high-frequency details. Extensive experiments demonstrate that VMESR achieves competitive performance in both objective metrics and perceptual quality compared to state-of-the-art methods, while significantly reducing parameter counts and computational cost. VMESR provides a practical balance between efficiency and reconstructive accuracy, offering a deployable super-resolution solution for embedded automotive sensors and enhancing the robustness of autonomous driving perception pipelines. Full article

The automatic modulation recognition (AMR) of low probability intercept (LPI) signals has received a considerable amount of interest from many researchers who have done much work on electronic reconnaissance. This recognition technology aims to design a classifier that enables the identification of signals with different modulation types. Based on deep learning models such as a convolutional neural network (CNN), the time-frequency images (TFIs) of the signal can be input to further extract features for classification. To improve recognition accuracy, especially under low signal-to-noise ratios (SNRs), we propose an AMR method for radio frequency proximity sensor signals based on a TFI enhancement network. The TFIs are denoised based on a per-pixel kernel prediction network (KPN), which can improve the quality of TFIs and achieves comparable denoising performance to traditional TFI reconstruction methods (e.g., sparse representation-based methods and low-rank approximation methods), while requiring significantly less computational overhead. The denoised TFIs, with enhanced signal quality and reduced noise, are then fed into the RetinalNet-based classifier as high-quality input features. This enhancement is crucial for the subsequent recognition stage, as it significantly improves the modulation recognition accuracy, particularly under challenging low SNR conditions. Simulation results show that the proposed method can accurately identify the modulation types of different radio frequency proximity sensors that are aliased in the time-frequency domain under low SNRs, and the average recognition accuracy rate of the signal remains above 97% when the signal-to-noise ratio is above −10 dB. Full article

Due to hardware limitations of Thermal InfraRed (TIR) cameras, TIR images captured by Unmanned Aerial Vehicles (UAVs) suffer from Low Resolutions (LRs) and blurred textures. Improving the spatial resolution of TIR images is of great significance for subsequent applications. Existing image Super-Resolution (SR) methods rely on High-Resolution (HR) ground truth for supervised training, resulting in limited generalization and a lack of mechanisms to preserve the physical consistency of thermal radiation. To address these two issues, this paper proposes an unsupervised super-resolution framework for UAV TIR imagery that integrates diffusion modeling with cross-modal texture transfer. The diffusion model enables stable reconstruction of the fundamental TIR structure without requiring high-resolution supervision, while multi-scale textures extracted from visible (VIS) imagery via Multi-Stage Decomposition based on Latent Low-Rank Representation (MS-DLatLRR) compensate for missing details. To suppress temperature distortions introduced by cross-modal texture transfer, a physics-guided constraint termed Prior-Informed Emissivity-Guided Coefficient Mapping (PI-EGCM) is incorporated. Emissivity-aware guidance maps constructed via semantic classification regulate texture transfer and preserve thermal radiation consistency. Experimental results demonstrate that the proposed method improves spatial resolution and perceptual quality while effectively maintaining temperature fidelity, achieving a balanced enhancement of structural detail and physical consistency. Full article

With the rapid development of emerging applications such as the Internet of Things (IoT) and distributed visual perception, massive amounts of correlated image data require efficient transmission under constrained bandwidth and noisy channel conditions. Although Shannon’s separation theorem provides a theoretically optimal basis for independent source-channel design, end-to-end joint optimization methods demonstrate higher performance potential in finite block length scenarios. This paper addresses the distributed image transmission problem with source correlation by proposing a Deep Joint Source-Channel Coding (DeepJSCC)-based framework. The scheme introduces a correlation feature extraction module at the receiver to uncover similarities among multiple sources and assist image reconstruction. Experimental results demonstrate that this method significantly improves reconstruction quality across various signal-to-noise ratios (SNRs), particularly excelling under small bandwidth ratios. Full article

This article explores the potential of the emerging image compression standard AV1 Image File Format (AVIF) as a format for storing 2D texture data in 3D computer graphics, aiming to assess its suitability for graphics applications. It presents a comparative performance evaluation, focusing on image quality, compression efficiency, and processing times, by comparing AVIF with the traditional format JPEG and the texture compression schemes BPTC and S3TC. To conduct the evaluation, a selected set of test images is compressed into the specified formats, loaded as textures, and assessed in a mockup 3D application to evaluate their visual performance in a realistic rendering context. The results show that AVIF delivers better fidelity to the original image compared to JPEG, BPTC, and S3TC, while also yielding a smaller file size. It outperforms JPEG by 9.2 dB in visual quality and by 174.4% in compression ratio, on average. However, this comes at the cost of longer processing times, with AVIF taking 126 times longer than JPEG and 185 times longer than S3TC to encode an image. AVIF also showed a 536% increase in decoding time compared to JPEG. BPTC produced high-fidelity images, second only to AVIF, but it required longer encoding times, depending on the quality settings. However, unlike AVIF, it offers GPU optimization benefits. Full article

The performance of deep-learning-based segmentation models is strongly dependent on the quality of the input data, which is frequently heterogeneous or degraded in real-world medical imaging scenarios. This work presents a data-centric algorithmic pipeline designed to improve cardiac MRI segmentation accuracy through systematic image enhancement and automatic slice-quality filtering. The proposed method is formalized as deterministic algorithm that combines image processing and supervised learning components. The approach integrates a contrast- and structure-preserving enhancement stage, based on bilateral filtering and adaptive histogram equalization, with a quality-aware selection algorithm. Slice quality is assessed using anatomical attributes extracted via YOLOv11s-based localization and a supervised classification model trained to identify diagnostically reliable images. When applied to transformer-based segmentation architectures such as ViTUNeT, the pipeline yields consistent improvements across all evaluation metrics without increasing model complexity or training cost. These findings emphasize the importance of algorithmic data curation as an effective strategy for enhancing robustness and stability in deep-learning segmentation pipelines and demonstrate the broader applicability of the proposed approach to computer-vision tasks involving heterogeneous or low-quality image datasets. Full article

We investigate the potential of frequent repeat imagery acquired by the PlanetScope Dove small satellite constellation to overcome temporal and spatial limitations in automated surface topography mapping. While individual PlanetScope Dove stereo pairs produce low-quality Digital Surface Models (DSMs) with large height uncertainties, the high temporal frequency enables multiple DSMs to enhance accuracy through multiple-pair image matching. We present a fully automated SETSM framework by improving the quality of PlanetScope Dove DSMs based on SETSM Multi-Pair Matching Procedure (SETSM MMP). This framework enhances stereo pair quality through an optimized stereo pair selection by sequential conditional filtering and a Weighted Stereo Pair Index (WSPI). A novel inter-plane vertical coregistration, which minimizes scaling errors between single stereo pair DSMs, was developed to improve consistency and accuracy in DSM quality without reference surfaces. Applied to the cloud-obscured Pantasma crater region in Nicaragua, the optimized stereo pair selection automatically selects well-defined stereo pairs. The inter-plane vertical coregistration without existing reference surfaces achieves up to a 43% Root Mean Square Error (RMSE) reduction and 26% improvement in distribution within a 5 m vertical error. DSM quality correlated strongly with tile size, stereo pair convergence angle, asymmetric angle and terrain-dependent scale variability. The proposed framework provides fully automatic, high quality PlanetScope Dove DSMs without Ground Control Points (GCPs). Full article

Accurate temperature control in ceramic roller kilns is critical for ensuring product quality; however, it remains challenging due to nonlinear thermal dynamics and the spatial lag inherent in traditional contact-based sensors. To address the limitations of sparse wall-mounted thermocouples and optical interference in kiln images, this paper presents a multimodal spatiotemporal fusion network (MST-FusionNet) for noncontact temperature detection of ceramic bodies on roller tracks. The proposed network integrates in-furnace combustion image sequences with distributed thermocouple measurements. First, a physics-informed pseudo-heatmap generation strategy based on Gaussian distributions is introduced to align discrete thermocouple readings with visual features, enabling effective early-stage multimodal fusion. Second, a residual compensation mechanism uses thermocouple data as a stable reference to learn local temperature deviations from visual and temporal features. In addition, an attention-enhanced LSTM module is employed to model combustion dynamics and suppress unreliable frames caused by smoke and flame fluctuations. Experimental results on a real industrial dataset show that the proposed method achieves a mean absolute error of 0.9164 °C and a root mean squared error of 1.2422 °C, demonstrating better performance than single-modal methods and simple fusion baselines. The proposed framework exhibits stable spatial characteristics across different roller positions and helps bridge the spatial discrepancy between boundary measurements and the actual thermal state of ceramic products, providing an effective solution for temperature detection in roller kilns. Full article

Soil amendments are widely promoted to improve turfgrass performance and soil properties in suboptimal soil; however, their effectiveness under field-managed conditions remains unclear. Two concurrent field experiments (i.e., turfgrass and soil and reduced nitrogen) were conducted from August 2022 to November 2023 in Gainesville, FL, using a randomized complete block design to evaluate organic and biological amendments under standard and reduced nitrogen (N) fertilization. In the turfgrass and soil portion, treatments included granular humic + fertilizer, liquid humic + fertilizer, biochar + fertilizer, microbial inoculant + fertilizer, compost, natural fertilizer, fertilizer, and a non-treated control. In the reduced N experiment, fertilizer rates for all amendment combinations and the natural fertilizer were applied at 50% (12.2 kg N ha⁻¹), while the full-rate fertilizer (24.4 kg N ha⁻¹) and non-treated control were included for comparison. Treatments were applied to St. Augustinegrass (Stenotaphrum secundatum (Walt.) Kuntze) and zoysiagrass (Zoysia spp. Willd.) established on a sand-based root zone. Turfgrass performance was assessed using visual quality, normalized difference vegetation index, and percent green via digital image analysis. Soil properties were evaluated using physical, chemical, and biological parameters. Treatment responses varied by amendment type and N rate. All treatments improved turfgrass performance relative to the non-treated control, with compost producing the greatest improvements in turfgrass quality and soil properties, including organic matter, pH, and plant-available P, K, and Fe. Humic substances, biochar, and microbial inoculants primarily increased potentially mineralizable N but provided limited improvements in turfgrass performance compared with fertilizer alone. Nitrogen rate was the primary determinant of turfgrass performance, with full N treatments producing the highest quality. Although reduced N treatments improved turfgrass quality relative to the control, amendment additions did not consistently enhance turfgrass performance under reduced N conditions. Full article