Search Results (5,726)

This paper presents a priori knowledge-based low-light image enhancement framework, termed Priori DCE ( Priori Deep Curve Estimation). The priori knowledge consists of two key aspects: (1) enhancing a low-light image is an ill-posed task, as the brightness of the enhanced image corresponding to a low-light image is uncertain. To resolve this issue, we incorporate priori channels into the model to guide the brightness of the enhanced image; (2) during the enhancement of a low-light image, the brightness of pixels may increase or decrease. This paper explores the probability of a pixel’s brightness increasing/decreasing as its prior enhancement /suppression probability. Intuitively, pixels with higher brightness should have a higher priori suppression probability, while pixels with lower brightness should have a higher priori enhancement probability. Inspired by this, we propose an enhancement function that adaptively adjusts the priori enhancement probability based on variations in pixel brightness. In addition, we propose the Global-Attention Block (GA Block). The GA Block ensures that, during the low-light image enhancement process, each pixel in the enhanced image is computed based on all the pixels in the low-light image. This approach facilitates interactions between all pixels in the enhanced image, thereby achieving visual balance. The experimental results on the LOLv2-Synthetic dataset demonstrate that Priori DCE has a significant advantage. Specifically, compared to the SOTA Retinexformer, the Priori DCE improves the PSNR index and SSIM index from 25.67 and 92.82 to 29.49 and 93.6, respectively, while the NIQE index decreases from 3.94 to 3.91. Full article

Hyperspectral image (HSI) denoising is challenged by complex spatial-spectral noise coupling. Existing deep learning methods, primarily designed for RGB images, fail to address HSI-specific noise distributions and spectral correlations. This paper proposes a Hybrid-Domain Synergistic Transformer (HDST) integrating frequency-domain enhancement and multiscale modeling. Key contributions include (1) a Fourier-based preprocessing module decoupling spectral noise; (2) a dynamic cross-domain attention mechanism adaptively fusing spatial-frequency features; and (3) a hierarchical architecture combining global noise modeling and detail recovery. Experiments on realistic and synthetic datasets show HDST outperforms state-of-the-art methods in PSNR, with fewer parameters. Visual results confirm effective noise suppression without spectral distortion. The framework provides a robust solution for HSI denoising, demonstrating potential for high-dimensional visual data processing. Full article

Synthetic Aperture Radar (SAR) target detection models are highly vulnerable to adversarial attacks, which significantly reduce detection performance and robustness. Existing adversarial SAR target detection approaches mainly focus on the image domain and neglect the critical role of signal propagation, making it difficult to fully capture the connection between the physical space and the image domain. To address this limitation, we propose an Echo Signal-Guided Adversarial Example Generation method for SAR target detection (SAR-ESAE). The core idea is to embed adversarial perturbations into SAR echo signals and propagate them through the imaging and inverse scattering processes, thereby establishing a unified attack framework across the signal, image, and physical spaces. In this way, perturbations not only appear as pixel-level distortions in SAR images but also alter the scattering characteristics of 3D target models in the physical space. Simulation experiments in the Scenario-SAR dataset demonstrate that the SAR-ESAE method reduces the mean Average Precision of the YOLOv3 model by 23.5% and 8.6% compared to Dpatch and RaLP attacks, respectively. Additionally, it exhibits excellent attack effectiveness in both echo signal and target model attack experiments and exhibits evident adversarial transferability across detection models with different architectures, such as Faster-RCNN and FCOS. Full article

Osteoarthritis (OA) is a multifactorial chronic musculoskeletal disorder characterised by cartilage degradation, synovial inflammation, and subchondral bone remodelling. Conventional diagnostic modalities, including radiographic imaging and symptom-based assessments, primarily detect disease in its later stages, limiting the potential for timely intervention. Inflammatory biomarkers, particularly Interleukin-6 (IL-6), Tumour Necrosis Factor-alpha (TNF-α), and Myeloperoxidase (MPO), have emerged as biologically relevant indicators of disease activity, with potential applications as companion diagnostics in precision medicine. This review examines the diagnostic and prognostic relevance of IL-6, TNF-α, and MPO in OA, focusing on their mechanistic roles in inflammation and joint degeneration, particularly through the activity of fibroblast-like synoviocytes (FLSs). The influence of sample type (serum, plasma, synovial fluid) and analytical performance, including enzyme-linked immunosorbent assay (ELISA), is discussed in the context of biomarker detectability. Advanced statistical and computational methodologies, including rank-based analysis of covariance (ANCOVA), discriminant function analysis (DFA), and Cox proportional hazards modelling, are explored for their capacity to validate biomarker associations, adjust for demographic variability, and stratify patient risk. Further, the utility of synthetic data generation, hierarchical clustering, and dimensionality reduction techniques (e.g., t-distributed stochastic neighbour embedding) in addressing inter-individual variability and enhancing model generalisability is also examined. Collectively, this synthesis supports the integration of biomarker profiling with advanced analytical modelling to improve early OA detection, enable patient-specific classification, and inform the development of targeted therapeutic strategies. 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

This study presents a segmentation framework for images of 220 kV cable insulation that addresses sample scarcity and blurred boundaries. The framework integrates data augmentation using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and the TransUNet architecture. Considering the difficulty and high cost of obtaining real cable images, WGAN-GP generates high-quality synthetic data to expand the dataset and improve the model’s generalization. The TransUNet network, designed to handle the structural complexity and indistinct edge features of insulation layers, combines the local feature extraction capability of convolutional neural networks (CNNs) with the global context modeling strength of Transformers. This combination enables accurate delineation of the insulation regions. The experimental results show that the proposed method achieves mDice, mIoU, MP, and mRecall scores of 0.9835, 0.9677, 0.9840, and 0.9831, respectively, with improvements of approximately 2.03%, 3.05%, 2.08%, and 1.98% over a UNet baseline. Overall, the proposed approach outperforms UNet, Swin-UNet, and Attention-UNet, confirming its effectiveness in delineating 220 kV cable insulation layers under complex structural and data-limited conditions. Full article

Objective: The aim of this study was to evaluate the accuracy of machine and deep learning approaches on radiomics features obtained by Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and contrast enhanced mammography (CEM) in the characterization of breast cancer and in the prediction of the tumor molecular profile. Methods: A total of 153 patients with malignant and benign lesions were analyzed and underwent MRI examinations. Considering the histological findings as the ground truth, three different types of findings were used in the analysis: (1) benign versus malignant lesions; (2) G1 + G2 vs. G3 classification; (3) the presence of human epidermal growth factor receptor 2 (HER2+ vs. HER2−). Radiomic features (n = 851) were extracted from manually segmented regions of interest using the PyRadiomics platform, following IBSI-compliant protocols. Highly correlated features were excluded, and the remaining features were standardized using z-score normalization. A feature selection process based on Elastic Net regularization (α = 0.5) was used to reduce dimensionality. Synthetic balancing of the training data was applied using the ROSE method to address class imbalance. Model performance was evaluated using repeated 10-fold cross-validation and AUC-based metrics. Results: Among the 153 patients enrolled in the studies, 113 were malignant lesions. Among the 113 malignant lesions, 32 had high grading (G3) and 66 had the HER2+ receptor. Radiomic features derived from both CEM and DCE-MRI showed strong discriminative performance for malignancy detection, with several features achieving AUCs above 0.80. Gradient Boosting Machine (GBM) achieved the highest accuracy (0.911) and AUC (0.907) in differentiating benign from malignant lesions. For tumor grading, the neural network model attained the best accuracy (0.848), while LASSO yielded the highest sensitivity (0.667) for detecting high-grade tumors. In predicting HER2+ status, the neural network also performed best (AUC = 0.669), with a sensitivity of 0.842. Conclusions: Radiomics-based machine learning models applied to multiparametric CEM and DCE-MRI images offer promising, non-invasive tools for breast cancer characterization. The models effectively distinguished benign from malignant lesions and showed potential in predicting histological grade and HER2 status. These results demonstrate that radiomic features extracted from CEM and DCE-MRI, when analyzed through machine and deep learning models, can support accurate breast cancer characterization. Such models may assist clinicians in early diagnosis, histological grading, and biomarker assessment, potentially enhancing personalized treatment planning and non-invasive decision-making in routine practice. Full article

Background: Dental caries arise from a multifactorial interplay between microbial dysbiosis, host immune responses, and enamel degradation visible on radiographs. Deep learning excels in image-based caries detection; however, integrative analyses that combine radiographic, microbiome, and transcriptomic data remain rare because public cohorts are seldom aligned. Objective: To determine whether three independent deep-learning pipelines—radiographic segmentation, microbiome regression, and transcriptome regression—can be reproducible implemented on non-aligned datasets, and to demonstrate the feasibility of estimating microbiome heritability in a matched twin cohort. Methods: (i) A U-Net with ResNet-18 encoder was trained on 100 annotated panoramic radiographs to generate a continuous caries-severity score from a predicted lesion area. (ii) Feed-forward neural networks (FNNs) were trained on supragingival 16S rRNA profiles (81 samples, 750 taxa) and gingival transcriptomes (247 samples, 54,675 probes) using randomly permuted severity scores as synthetic targets to stress-test preprocessing, training, and SHAP-based interpretability. (iii) In 49 monozygotic and 50 dizygotic twin pairs (n = 198), Bray–Curtis dissimilarity quantified microbial heritability, and an FNN was trained to predict recorded TotalCaries counts. Results: The U-Net achieved IoU = 0.564 (95% CI 0.535–0.594), precision = 0.624 (95% CI 0.583–0.667), recall = 0.877 (95% CI 0.827–0.918), and correlated with manual severity scores (r = 0.62, p < 0.01). The synthetic-target FNNs converged consistently but—as intended—showed no predictive power (R² ≈ −0.15 microbiome; −0.18 transcriptome). Twin analysis revealed greater microbiome similarity in monozygotic versus dizygotic pairs (0.475 ± 0.107 vs. 0.557 ± 0.117; p = 0.0005) and a modest correlation between salivary features and caries burden (r = 0.25). Conclusions: Modular deep-learning pipelines remain computationally robust and interpretable on non-aligned datasets; radiographic severity provides a transferable quantitative anchor. Twin-cohort findings confirm heritable patterns in the oral microbiome and outline a pathway toward future clinical translation once patient-matched multi-omics are available. This framework establishes a scalable, reproducible foundation for integrative caries research. Full article

Inverse Synthetic Aperture Radar (ISAR) imaging plays a crucial role in reconnaissance and target monitoring. However, the presence of uncertain factors often leads to indistinct component visualization and significant noise contamination in imaging results, where weak scattering components are frequently submerged by noise. To address these challenges, this paper proposes a Phase-Aware Multi-Scale Transformer network (PA-MSFormer) that simultaneously enhances weak component regions and suppresses noise. Unlike existing methods that struggled with this fundamental trade-off, our approach achieved 70.93 dB PSNR on electromagnetic simulation data, surpassing the previous best method by 0.6 dB, while maintaining only 1.59 million parameters. Specifically, we introduce a phase-aware attention mechanism that separates noise from weak scattering features through complex-domain modulation, a dual-branch fusion network that establishes frequency-domain separability criteria, and a progressive gate fuser that achieves pixel-level alignment between high- and low-frequency features. Extensive experiments on electromagnetic simulation and real-measured datasets demonstrate that PA-MSFormer effectively suppresses noise while significantly enhancing target visualization, establishing a solid foundation for subsequent interpretation tasks. Full article

The integrity of subsea oil and gas pipelines is essential for offshore safety and environmental protection. Conventional leak detection approaches, such as manual inspection and indirect sensing, are often costly, time-consuming, and prone to subjectivity, motivating the development of automated methods. In this study, we present a deep learning-based framework for detecting underwater leaks using images acquired in controlled experiments designed to reproduce representative conditions of subsea monitoring. The dataset was generated by simulating both gas and liquid leaks in a water tank environment, under scenarios that mimic challenges observed during Remotely Operated Vehicle (ROV) inspections along the Brazilian coast. It was further complemented with artificially generated synthetic images (Stable Diffusion) and publicly available subsea imagery. Multiple Convolutional Neural Network (CNN) architectures, including VGG16, ResNet50, InceptionV3, DenseNet121, InceptionResNetV2, EfficientNetB0, and a lightweight custom CNN, were trained with transfer learning and evaluated on validation and blind test sets. The best-performing models achieved stable performance during training and validation, with macro F1-scores above 0.80, and demonstrated improved generalization compared to traditional baselines such as VGG16. In blind testing, InceptionV3 achieved the most balanced performance across the three classes when trained with synthetic data and augmentation. The study demonstrates the feasibility of applying CNNs for vision-based leak detection in complex underwater environments. A key contribution is the release of a novel experimentally generated dataset, which supports reproducibility and establishes a benchmark for advancing automated subsea inspection methods. Full article

Aiming at the problems of detail blurring and structural distortion caused by speckle noise, additive white noise and hybrid noise interference in synthetic aperture radar (SAR) images, this paper proposes a Dynamic Adaptive Transformer Network (DAT-Net) integrating a dynamic gated attention module and a frequency-domain multi-expert enhancement module for SAR image denoising. The proposed model leverages a multi-scale encoder–decoder framework, combining local convolutional feature extraction with global self-attention mechanisms to transcend the limitations of conventional approaches restricted to single noise types, thereby achieving adaptive suppression of multi-source noise contamination. Key innovations comprise the following: (1) A Dynamic Gated Attention Module (DGAM) employing dual-path feature embedding and dynamic thresholding mechanisms to precisely characterize noise spatial heterogeneity; (2) A Frequency-domain Multi-Expert Enhancement (FMEE) Module utilizing Fourier decomposition and expert network ensembles for collaborative optimization of high-frequency and low-frequency components; (3) Lightweight Multi-scale Convolution Blocks (MCB) enhancing cross-scale feature fusion capabilities. Experimental results demonstrate that DAT-Net achieves quantifiable performance enhancement in both simulated and real SAR environments. Compared with other denoising algorithms, the proposed methodology exhibits superior noise suppression across diverse noise scenarios while preserving intrinsic textural features. Full article

Underwater Object Detection (UOD) based on Synthetic Aperture Sonar (SAS) images is one of the core tasks of underwater intelligent perception systems. However, the existing UOD methods suffer from excessive model redundancy, high computational demands, and severe image quality degradation due to noise. To mitigate these issues, this paper proposes an ultra-lightweight and high-precision underwater object detection method for SAS images. Based on a single-stage detection framework, four efficient and representative lightweight modules are developed, focusing on three key stages: feature extraction, feature fusion, and feature enhancement. For feature extraction, the Dilated-Attention Aggregation Feature Module (DAAFM) is introduced, which leverages a multi-scale Dilated Attention mechanism for strengthening the model’s capability to perceive key information, thereby improving the expressiveness and spatial coverage of extracted features. For feature fusion, the Channel–Spatial Parallel Attention with Gated Enhancement (CSPA-Gate) module is proposed, which integrates channel–spatial parallel modeling and gated enhancement to achieve effective fusion of multi-level semantic features and dynamic response to salient regions. In terms of feature enhancement, the Spatial Gated Channel Attention Module (SGCAM) is introduced to strengthen the model’s ability to discriminate the importance of feature channels through spatial gating, thereby improving robustness to complex background interference. Furthermore, the Context-Aware Feature Enhancement Module (CAFEM) is designed to guide feature learning using contextual structural information, enhancing semantic consistency and feature stability from a global perspective. To alleviate the challenge of limited sample size of real sonar images, a diffusion generative model is employed to synthesize a set of pseudo-sonar images, which are then combined with the real sonar dataset to construct an augmented training set. A two-stage training strategy is proposed: the model is first trained on the real dataset and then fine-tuned on the synthetic dataset to enhance generalization and improve detection robustness. The SCTD dataset results confirm that the proposed technique achieves better precision than the baseline model with only 10% of its parameter size. Notably, on a hybrid dataset, the proposed method surpasses Faster R-CNN by 10.3% in mAP50 while using only 9% of its parameters. Full article

Fermentation has been a crucial process in the preparation of foods and beverages for consumption, especially for the purpose of adding value to nutrients and bioactive compounds; however, conventional approaches have certain drawbacks such as not being able to fulfill the requirements of the ever-increasing global population as well as the sustainability goals. This review aims to evaluate how the application of advanced fermentation techniques can transform the food production system to be more effective, nutritious, and environmentally friendly. The techniques discussed include metabolic engineering, synthetic biology, AI-driven fermentation, quorum sensing regulation, and high-pressure processing, with an emphasis on their ability to enhance microbial activity with a view to enhancing product output. Authentic, wide-coverage scientific research search engines were used such as Google Scholar, Research Gate, Science Direct, PubMed, and Frontiers. The literature search was carried out for reports, articles, as well as papers in peer-reviewed journals from 2010 to 2024. A statistical analysis with a graphical representation of publication trends on the main topics was conducted using PubMed data from 2010 to 2024. In this present review, 112 references were used to investigate novel fermentation technologies that fortify the end food products with nutritional and functional value. Images that illustrate the processes involved in novel fermentation technologies were designed using Adobe Photoshop. The findings indicate that, although there are issues regarding costs, the scalability of the process, and the acceptability of the products by the consumers, the technologies provide a way of developing healthy foods and products produced using sustainable systems. This paper thus calls for more research and development as well as for the establishment of a legal frameworks to allow for the integration of these technologies into the food production system and make the food industry future-proof. Full article

In the context of reservoir monitoring, time-lapse (4D) full-waveform inversion (FWI) of seismic data can potentially estimate reservoir changes with high resolution. However, most existing field-data applications are carried out with isotropic, and often acoustic, FWI algorithms. Here, we apply a time-lapse FWI methodology for transversely isotropic (TI) media with a vertical symmetry axis (VTI) to offshore streamer data acquired at Pyrenees field in Australia. We explore different objective functions, including those based on global correlation (GC) and designed to mitigate errors in the source signature (SI, or source-independent). The GC objective function, which utilizes mostly phase information, produces the most accurate inversion results by mitigating the difficulties associated with amplitude matching of the synthetic and field data. The SI FWI algorithm is generally more robust in the presence of distortions in the source wavelet than the other two methods, but its application to field data is hampered by reliance on amplitude matching. Taking anisotropy into account provides a better fit to the recorded data, especially at far offsets. In addition, the application of the anisotropic FWI improves the flatness of the major reflection events in the common-image gathers (CIGs). The 4D response obtained by FWI reveals time-lapse parameter variations likely caused by the reservoir gas coming out of solution and by the replacement of gas with oil. Full article

Background/Objectives: Survivors of sepsis can develop left ventricular (LV) systolic function with focal myocardial fibrosis. The relationship between diffuse myocardial fibrosis or oedema and LV systolic function remains unknown in this patient cohort. This study sought to address this knowledge gap using cardiovascular magnetic resonance (CMR) parametric mapping methods. Methods: Sepsis survivors who underwent CMR at a UK cardiac centre were included. CMR images analysed include cines, native T1-mapping, native T2-mapping, and post-contrast T1-mapping. Synthetic extracellular volume (ECV) fraction was also estimated. Native myocardial T1 values, native myocardial T2 values, and ECV values were compared against LV ejection fraction (LVEF). Results: Of the 37 sepsis survivors (age 53 ± 16 years old; 57% males), the mean left ventricular ejection fraction (LVEF) was 55% (IQR 43–62), and 43% of the patients had LV systolic dysfunction (LVEF < 50%). Mean native myocardial T1 values were 1055 ± 65 ms (septal) and 1051 ± 60 ms (global). Mean synthetic ECV values were 0.30 ± 0.04. Mean native myocardial T2 values were 52 ± 7 ms (septal) and 53 ± 6 ms (global). Septal and global native myocardial T1 values were not significantly correlated with LVEF (rho = 0.080, p = 0.637; rho = 0.036, p = 0.831, respectively). Synthetic ECV was not significantly correlated to LVEF (rho = −0.082; p = 0.723). Septal and global native myocardial T2 values were weakly correlated with LVEF (rho = 0.261, p = 0.281; rho = 0.216, p = 0.375, respectively). On ROC analysis, the performance of native myocardial T1 values, ECV, and native myocardial T2 values for predicting LV dysfunction was modest (AUC: 0.53 ± 0.10, 0.54 ± 11, and 0.68 ± 0.14; all p > 0.05, respectively). Conclusions: CMR markers of diffuse myocardial fibrosis (native T1-mapping and ECV) and myocardial oedema (native T2-mapping) have weak relationships with left ventricular systolic function in this study cohort of sepsis survivors. Further work is needed to better assess the role of diffuse myocardial fibrosis and oedema in the pathophysiology of post-sepsis cardiomyopathy. Full article