Search Results (247)

Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article

Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference ⁴⁰K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger–Mueller counter (G–M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGR_G–M and spectrometry-based ⁴⁰K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from ⁴⁰K using the theoretical value of α = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGR_G–M measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGR_G–M measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGR_G–M than on ⁴⁰K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGR_G–M and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

Background: Leigh’s Disease is a rare mitochondrial disorder primarily affecting the central nervous system, with frequent secondary cardiac manifestations such as hypertrophic and dilated cardiomyopathies. Early detection of cardiac complications is crucial for patient management, but manual interpretation of cardiac MRI is labour-intensive and subject to inter-observer variability. Methodology: We propose an integrated deep learning framework using cardiac MRI to automate the detection of cardiac abnormalities associated with Leigh’s Disease. Four CNN architectures—Inceptionv3, a custom 3-layer CNN, DenseNet169, and EfficientNetB2—were trained on preprocessed MRI data (224 × 224 pixels), including left ventricular segmentation, contrast enhancement, and gamma correction. Morphological features (area, aspect ratio, and extent) were also extracted to aid interpretability. Results: EfficientNetB2 achieved the highest test accuracy (99.2%) and generalization performance, followed by DenseNet169 (98.4%), 3-layer CNN (95.6%), and InceptionV3 (94.2%). Statistical morphological analysis revealed significant differences in cardiac structure between Leigh’s and non-Leigh’s cases, particularly in area (212,097 vs. 2247 pixels) and extent (0.995 vs. 0.183). The framework was validated using ROC (AUC = 1.00), Brier Score (0.000), and cross-validation (mean sensitivity = 1.000, std = 0.000). Feature embedding visualisation using PCA, t-SNE, and UMAP confirmed class separability. Grad-CAM heatmaps localised relevant myocardial regions, supporting model interpretability. Conclusions: Our deep learning-based framework demonstrated high diagnostic accuracy and interpretability in detecting Leigh’s disease-related cardiac complications. Integrating morphological analysis and explainable AI provides a robust and scalable tool for early-stage detection and clinical decision support in rare diseases. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article

The point-kernel (PK) technique has a long history in applied radiation shielding, originating from the early days of digital computers. The PK technique applied to gamma-ray attenuation is one of many successful applications, based on the linear superposition principle applied to distributed radiation sources. Mathematically speaking, the distributed source will produce a detector response equivalent to the numerical integration of the radiation received from an equivalent number of point sources. In this treatment, there is no interference between individual point sources, while inherent limitations of the PK method are its inability to simulate gamma scattering in shields and the usage of simple boundary conditions. The PK method generally works for gamma-ray shielding with corrective B-factor for scattering and only specifically for fast neutron attenuation in a hydrogenous medium with the definition of cross section removal. This paper presents theoretical and programming aspects of the PK program developed for a distributed source of photons (line, disc, plane, sphere, slab volume, etc.) and slab shields. The derived flux solutions go beyond classical textbooks as they include the analytical integration of Taylor B-factor, obtaining a closed form readily suitable for programming. The specific computational modules are unified with a graphical user interface (GUI), assisting users with input/output data and visualization, developed for the fast radiological characterization of simple shielding problems. Numerical results of the selected PK test cases are presented and verified with the CADIS hybrid shielding methodology of the MAVRIC/SCALE6.1.3 code package from the ORNL. Full article

Marine oil spills cause significant environmental damage worldwide. Marine radar imagery is used for oil spill detection. However, the rapid attenuation of backscatter intensity with increasing distance limits detectable coverage. A multi-stage image enhancement framework integrating background clutter fitting subtraction, Multi-Scale Retinex, and Gamma correction is proposed. Experimental results using marine radar images sampled in the oil spill incident in Dalian 2010 are used to demonstrate the significant improvements. Compared to Contrast-Limited Adaptive Histogram Equalization and Partially Overlapped Sub-block Histogram Equalization, the proposed method enhances image contrast by 24.01% and improves the measurement of enhancement by entropy by 17.11%. Quantitative analysis demonstrates 95% oil spill detection accuracy through visual interpretation, while significantly expanding detectable coverage for oil extraction. Full article

In recent years, the continuous advancement of deep learning technology and its integration into the domain of low-light image enhancement have led to a steady improvement in enhancement effects. However, this progress has been accompanied by an increase in model complexity, imposing significant constraints on applications that demand high real-time performance. To address this challenge, inspired by the state-of-the-art Zero-DCE approach, we introduce a novel method that transforms the low-light image enhancement task into a curve estimation task tailored to each individual image, utilizing a lightweight shallow neural network. Specifically, we first design a novel curve formula based on Gamma correction, which we call the Gamma-based light-enhancement (GLE) curve. This curve enables outstanding performance in the enhancement task by directly mapping the input low-light image to the enhanced output at the pixel level, thereby eliminating the need for multiple iterative mappings as required in the Zero-DCE algorithm. As a result, our approach significantly improves inference speed. Additionally, we employ a lightweight network architecture to minimize computational complexity and introduce a novel global channel attention (GCA) module to enhance the nonlinear mapping capability of the neural network. The GCA module assigns distinct weights to each channel, allowing the network to focus more on critical features. Consequently, it enhances the effectiveness of low-light image enhancement while incurring a minimal computational cost. Finally, our method is trained using a set of zero-reference loss functions, akin to the Zero-DCE approach, without relying on paired or unpaired data. This ensures the practicality and applicability of our proposed method. The experimental results of both quantitative and qualitative comparisons demonstrate that, despite its lightweight design, the images enhanced using our method not only exhibit perceptual quality, authenticity, and contrast comparable to those of mainstream state-of-the-art (SOTA) methods but in some cases even surpass them. Furthermore, our model demonstrates very fast inference speed, making it suitable for real-time inference in resource-constrained or mobile environments, with broad application prospects. Full article

Sugar apple (Annona squamosa) is prized for its excellent taste, rich nutrition, and diverse uses, making it valuable for both fresh consumption and medicinal purposes. Predominantly found in tropical regions of the Americas and Asia, its harvesting remains labor-intensive in orchard settings, resulting in low efficiency and high costs. This study investigates the use of computer vision for sugar apple instance segmentation and introduces an improved deep learning model, GCE-YOLOv9-seg, specifically designed for orchard conditions. The model incorporates Gamma Correction (GC) to enhance image brightness and contrast, improving target region identification and feature extraction in orchard settings. An Efficient Multiscale Attention (EMA) mechanism was added to strengthen feature representation across scales, addressing sugar apple variability and maturity differences. Additionally, a Convolutional Block Attention Module (CBAM) refined the focus on key regions and deep semantic features. The model’s performance was evaluated on a self-constructed dataset of sugar apple instance segmentation images captured under natural orchard conditions. The experimental results demonstrate that the proposed GCE-YOLOv9-seg model achieved an F1 score (F1) of 90.0%, a precision (P) of 89.6%, a recall (R) level of 93.4%, a mAP@0.5 of 73.2%, and a mAP@[0.5:0.95] of 73.2%. Compared to the original YOLOv9-seg model, the proposed GCE-YOLOv9-seg showed improvements of 1.5% in the F1 score and 3.0% in recall for object detection, while the segmentation task exhibited increases of 0.3% in mAP@0.5 and 1.0% in mAP@[0.5:0.95]. Furthermore, when compared to the latest model YOLOv12-seg, the proposed GCE-YOLOv9-seg still outperformed with an F1 score increase of 2.8%, a precision (P) improvement of 0.4%, and a substantial recall (R) boost of 5.0%. In the segmentation task, mAP@0.5 rose by 3.8%, while mAP@[0.5:0.95] demonstrated a significant enhancement of 7.9%. This method may be directly applied to sugar apple instance segmentation, providing a promising solution for automated sugar apple detection in natural orchard environments. Full article

For external inspection of electrical equipment, poor lighting conditions often lead to problems such as uneven illumination, insufficient brightness, and detail loss, which directly affect subsequent analysis. To solve this problem, the Retinex image enhancement method based on the Component Generation Network (CGNet) is proposed in this paper. It employs CGNet to accurately estimate and generate the illumination and reflection components of the target image. The CGNet, based on UNet, integrates Residual Branch Dual-convolution blocks (RBDConv) and the Channel Attention Mechanism (CAM) to improve the feature-learning capability. By setting different numbers of network layers, the optimal estimation of the illumination and reflection components is achieved. To obtain the ideal enhancement results, gamma correction is applied to adjust the estimated illumination component, while the HSV transformation model preserves color information. Finally, the effectiveness of the proposed method is verified on a dataset of poorly illuminated images from external inspection of electrical equipment. The results show that this method not only requires no external datasets for training but also improves the detail clarity and color richness of the target image, effectively addressing poor lighting of images in external inspection of electrical equipment. Full article

In this paper, we present three log-linear models for comparing spectral density functions (SDFs) of neural spike sequences (NSSs). The logarithmic (ln) ratios of the estimated SDFs are modeled as polynomial expressions with respect to angular frequencies plus residual series with autocorrelated errors. The advantage of the proposed models is that they can be applied within certain frequency ranges. Analysis of point processes in the frequency domain can be performed to obtain estimates of the SDFs of NSSs by smoothing the mean-corrected periodograms using moving average weighting schemes. The weighting schemes may differ in the estimated SDFs. To decorrelate the error terms in the log models, we apply a two-stage method: in the first stage, the error terms are identified by choosing a suitable model, while in the second stage, the reliable estimates of the unknown parameters involved in the polynomial expressions are derived by decorrelating the data. An illustrative example from the field of neurophysiology is described, in which the neuromuscular system of the muscle spindle is affected by three different stimuli: (a) a gamma motoneuron, (b) an alpha motoneuron, and (c) a combination of gamma and alpha motoneurons. It is shown that the effect of the gamma motoneuron on the muscle spindle is shifted by the presence of the alpha motoneuron to lower frequencies in the range of [1.03, 7.6] Hz, whereas the presence of the gamma motoneuron shifts the effect of the alpha motoneuron in two bands of frequencies: one in the range of [13.5, 19.9) Hz and the other in the range of [19.9, 30.8] Hz. Full article

In fog, rain, snow, haze, and other complex environments, environmental objects photographed by imaging equipment are prone to image blurring, contrast degradation, and other problems. The decline in image quality fails to satisfy the requirements of application scenarios such as video surveillance, satellite reconnaissance, and target tracking. Aiming at the shortcomings of the traditional dark channel prior algorithm in video defogging, this paper proposes a method to improve the guided filtering algorithm to refine the transmittance image and reduce the halo effect in the traditional algorithm. Meanwhile, a gamma correction method is proposed to recover the defogged image and enhance the image details in a low-light environment. The parallel symmetric pipeline design of the FPGA is used to improve the system’s overall stability. The improved dark channel prior algorithm is realized through the hardware–software co-design of ARM and the FPGA. Experiments show that this algorithm improves the Underwater Image Quality Measure (UIQM), Average Gradient (AG), and Information Entropy (IE) of the image, while the system is capable of stably processing video images with a resolution of 1280 × 720 @ 60 fps. By numerically analyzing the power consumption and resource usage at the board level, the power consumption on the FPGA is only 2.242 W, which puts the hardware circuit design in the category of low power consumption. Full article

The purpose of this study is to develop a micrograph-driven model for Ti6Al4V mechanical property prediction through integrated image preprocessing and deep learning, reducing the reliance on manually extracted features and process parameters. This paper systematically evaluates the capability of a CNN model using preprocessed micrographs to predict Ti6Al4V alloy ultimate tensile strength (UTS), while analyzing how different preprocessing combinations influence model performance. A total of 180 micrographs were selected from published literature to construct the dataset. After applying image standardization (grayscale transformation, resizing, and normalization) and image enhancement, a pre-trained ResNet34 model was employed with transfer learning to conduct strength grade classification (low, medium, high) and UTS regression. The results demonstrated that on highly heterogeneous micrograph datasets, the model exhibited moderate classification capability (maximum accuracy = 65.60% ± 1.22%) but negligible UTS regression capability (highest R² = 0.163 ± 0.020). Fine-tuning on subsets with consistent forming processes improved regression performance (highest R² = 0.360 ± 1.47 × 10⁻⁵), outperforming traditional predictive models (highest R² = 0.148). The classification model was insensitive to normalization methods, while min–max normalization with center-cropping showed optimal standardization for regression (R² = 0.111 ± 0.017). Gamma correction maximized classification accuracy, whereas histogram equalization achieved the highest improvement for regression. Full article

Low-light image enhancement remains a challenging task in computer vision due to the complex interplay of noise, asymmetrical artifacts, illumination non-uniformity, and detail preservation. Existing methods such as traditional histogram equalization, gamma correction, and Retinex-based approaches often struggle to balance contrast improvement and naturalness preservation. Deep learning methods such as CNNs and transformers have shown promise, but face limitations in modeling multi-scale illumination and long-range dependencies. To address these issues, we propose WIGformer, a novel wavelet-based illumination-guided transformer framework for low-light image enhancement. The proposed method extends the single-stage Retinex theory to explicitly model noise in both reflectance and illumination components. It introduces a wavelet illumination estimator with a Wavelet Feature Enhancement Convolution (WFEConv) module to capture multi-scale illumination features and an illumination feature-guided corruption restorer with an Illumination-Guided Enhanced Multihead Self-Attention (IGEMSA) mechanism. WIGformer leverages the symmetry properties of wavelet transforms to achieve multi-scale illumination estimation, ensuring balanced feature extraction across different frequency bands. The IGEMSA mechanism integrates adaptive feature refinement and illumination guidance to suppress noise and artifacts while preserving fine details. The same mechanism allows us to further exploit symmetrical dependencies between illumination and reflectance components, enabling robust and natural enhancement of low-light images. Extensive experiments on the LOL-V1, LOL-V2-Real, and LOL-V2-Synthetic datasets demonstrate that WIGformer achieves state-of-the-art performance and outperforms existing methods, with PSNR improvements of up to 26.12 dB and an SSIM score of 0.935. The qualitative results demonstrate WIGformer’s superior capability to not only restore natural illumination but also maintain structural symmetry in challenging conditions, preserving balanced luminance distributions and geometric regularities that are characteristic of properly exposed natural scenes. Full article

Background/Objectives: Intrahepatic cholangiocarcinoma (ICC) is subclassified into small and large duct types. Although these subclassifications may help determine the appropriate treatment strategy, subclassification diagnosis currently depends on postoperative pathological examinations. This study aimed to establish a nomogram to predict ICC subclassifications. Methods: This study included 126 patients with ICC who underwent liver resection. The participants were divided into small and large duct-type ICC groups. A nomogram to predict large duct-type ICC was developed using four diagnostic imaging findings: rim-type enhancement in the early phase, an absence of tumor enhancement in the early phase, the presence of peripheral biliary dilatation due to tumor invasion, the presence of penetrating Glisson’s vessels in the tumor, and two laboratory test results: serum gamma-glutamyl transpeptidase and carbohydrate antigen 19-9 levels. Nomogram performance was also assessed. Moreover, the bootstrap method and calibration plots were used to assess nomogram validity. Results: Seventy and fifty-six patients were pathologically diagnosed with small and large duct-type ICCs, respectively. The area under the curve of the established nomogram was 0.93 and remained 0.91 after Harrell’s bias correction. The sensitivity and specificity of the nomogram developed using the Youden index were higher than those of any of the characteristic imaging findings. Calibration plots demonstrated a strong association between the nomogram and the actual data. Conclusions: We developed a novel preoperative nomogram to predict large duct-type ICC. This nomogram can be clinically useful for predicting the subclassifications of ICCs and may contribute to the establishment of a more appropriate treatment strategy for ICC. Full article