Search Results (236)

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

The properties of light propagation underwater typically cause color distortion and reduced contrast in underwater images. In addition, complex underwater lighting conditions can result in issues such as non-uniform illumination, spotting, and noise. To address these challenges, we propose an innovative underwater-image enhancement (UIE) approach based on maximum information-channel compensation and edge-preserving filtering techniques. Specifically, we first develop a channel information transmission strategy grounded in maximum information preservation principles, utilizing the maximum information channel to improve the color fidelity of the input image. Next, we locally enhance the color-corrected image using guided filtering and generate a series of globally contrast-enhanced images by applying gamma transformations with varying parameter values. In the final stage, the enhanced image sequence is decomposed into low-frequency (LF) and high-frequency (HF) components via side-window filtering. For the HF component, a weight map is constructed by calculating the difference between the current exposedness and the optimum exposure. For the LF component, we derive a comprehensive feature map by integrating the brightness map, saturation map, and saliency map, thereby accurately assessing the quality of degraded regions in a manner that aligns with the symmetry principle inherent in human vision. Ultimately, we combine the LF and HF components through a weighted summation process, resulting in a high-quality underwater image. Experimental results demonstrate that our method effectively achieves both color restoration and contrast enhancement, outperforming several State-of-the-Art UIE techniques across multiple datasets. Full article

Introduction: The group of so-called “sellar-region masses” consists of a heterogeneous group of neoplasms and tumor-mimicking lesions, whose differential diagnosis may be challenging due to the overlapping of clinical and radiological features, which can be found both in “common” and “uncommon” lesions. The choice of a correct treatment strategy is still arduous and requires histological analysis. Gamma Knife Radiosurgery (GKRS) has already been reported as a safe and effective treatment in these cases. The objective of this study is to evaluate single-center pre-operative data, post-operative outcomes, and long-term follow-up in patients treated with GKRS for unusual sellar tumors. Methods: We retrospectively identified and analyzed nine patients treated with GKRS from 2004 to 2015, according to a standard protocol. Lesions consist of hypothalamic hamartoma (HH), Rathke’s cleft cist (RCC), Langerhans cell histiocytosis (LCH), spindle cell oncocytoma (SCO), choroid plexus papilloma (CPP), and ossifying fibroma (OF). The diagnosis was histologically confirmed in six patients that underwent surgery, while in three patients, diagnosis was based on characteristic clinical and radiological findings (two HH and one RCC). Pre-operative and post-operative data were retrieved from medical archives, and long-term follow-up was obtained through clinical and neuroradiological periodic examination. Results: In our series, all the “rare” sellar lesions treated, had a successful radiographic and clinical response in a medium-long follow-up period. Conclusions: The long-term follow-up results suggest that GKRS is a safe and effective treatment in rare sellar lesions, with very low toxicity. To the best of our knowledge, this report represents the largest series of unusual sellar lesions treated with GKRS in a single high-volume center, suggesting that GKRS might be an effective non-invasive adjuvant treatment option. Further studies and a larger number of patients are needed to confirm if residuals of these rare sellar lesions might regress on their own without treatment or if other non-invasive treatments could be as effective as GKRS. Full article

This study analyzes the performance of a multi-user digital communication system aided by reflecting intelligent surfaces (RIS) in terms of bit error probability and secrecy outage probability for a system sending symbols with M-QAM modulation passing through channels with Weibull fading, where RIS are employed to improve the signal-to-noise plus interference ratio (SINR) for each user. The performance analysis is conducted based on the statistical properties of the phase correction error of the transmitted signal, which follows a von Mises distribution. Furthermore, this study demonstrates that the resulting SINR follows a gamma distribution, with its parameters derived analytically. The RIS performance increases the line of sight strength and reduces the secrecy outage probability and error probability when the number of reflectors is sufficiently large, even without direct links between the users and the transmitter. Full article

Highly resolved and accurate daily precipitation data are required for impact models to perform adequately and correctly measure the impacts of high-risk events. In order to produce such data, bias correction is often needed. Most of those statistical methods correct the probability distributions of daily precipitation by modeling them with either empirical or parametric distributions. A recent semi-parametric model based on a penalized Berk–Jones (BJ) statistical test, which allows for automatic and personalized splicing of parametric and non-parametric distributions, has been developed. This method, called the Stitch-BJ model, was found to be able to model daily precipitation correctly and showed interesting potential in a bias correction setting. In the present study, we will consolidate these results by taking into account the seasonal properties of daily precipitation in an out-of-sample context and by considering dry days probabilities in our methodology. We evaluate the performance of the Stitch-BJ method in this seasonal bias correction setting against more classical models such as the Gamma, Exponentiated Weibull (ExpW), Extended Generalized Pareto (EGP) or empirical distributions. Results show that a seasonal separation of data is necessary in order to account for intra-annual non-stationarity. Moreover, the Stitch-BJ distribution was able to consistently perform as well as or better than all the other considered models over the validation set, including the empirical distribution, which is often used due to its robustness. Finally, while methods for correcting dry day probabilities can be easily applied, their relevance can be discussed as temporal and spatial correlations are often neglected. Full article

In this study, the effective radius of aerosol particles was experimentally retrieved using a self-developed dual-wavelength atmospheric aerosol lidar. A single-valued lookup table was first established, based on the OPAC database and the Gamma size distribution model, to define the relationship between the extinction coefficient ratio and the effective radius of atmospheric aerosol particles. The extinction coefficients corresponding to the 355 nm and 1064 nm wavelengths were then calculated using the echo signals retrieved horizontally by the lidar, in conjunction with the Mie scattering lidar equation. Subsequently, the lookup table was used to retrieve the real-time effective radius of aerosol particles by inputting the extinction coefficient ratio of the two wavelengths. Finally, the retrieval results were compared with the effective radii measured by an optical particle spectrometer, which had been corrected for relative humidity. An analysis over six months showed a coefficient of determination (R²) greater than 0.83. The results demonstrated that the dual-wavelength lidar exhibits a stable performance, the retrieval method is valid, and the detection results are accurate and reliable. Full article

Background and Objectives: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is a well-established technique for evaluating myocardial perfusion and function in patients with suspected or known coronary artery disease. While conventional dual-detector SPECT scanners have limitations in spatial resolution and photon detection sensitivity, recent advancements, including full-ring solid-state cadmium zinc telluride (CZT) detectors, offer enhanced image quality and improved diagnostic accuracy. This study aimed to compare the performance of Veriton-CT, a full-ring CZT SPECT system, with GE Discovery 530c, a dedicated cardiac fixed-angle gamma camera, in myocardial perfusion imaging and their correlation with coronary angiography findings. Materials and Methods: This was a prospective study that analyzed 21 patients who underwent MPI at the Department of Nuclear Medicine, Lithuanian University of Health Sciences, Kauno Klinikos. A one-day stress–rest protocol using 99mTc-Sestamibi was employed, with stress testing performed via bicycle ergometry or pharmacological induction. MPI was first conducted using GE Discovery 530c (GE Health Care, Boston, MA, USA), followed by imaging on Veriton-CT, which included low-dose CT for attenuation correction. The summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) were analyzed and compared between both imaging modalities. Coronary angiography results were retrospectively collected, and lesion-based analysis was performed to assess the correlation between imaging results and the presence of significant coronary artery stenosis (≥35% and ≥70% narrowing). Image quality and the certainty of distinguishing the inferior myocardial wall from extracardiac structures were also evaluated by two independent researchers with differing levels of experience. Results: Among the 14 patients included in the final analysis, Veriton-CT was more likely to classify MPI scans as normal (64.3%) compared to GE Discovery 530c (28.6%). Additionally, Veriton-CT provided a better assessment of the right coronary artery (RCA) basin, showing greater agreement with coronary angiography findings than GE Discovery 530c, although the difference was not statistically significant. No significant differences in lesion overlap were observed for the left anterior descending artery (LAD) or left circumflex artery (LCx) basins. Furthermore, the image quality assessment revealed slightly better delineation of extracardiac structures using Veriton-CT (Spectrum Dynamics Medical, Caesarea, Israel), particularly when evaluated by an experienced researcher. However, no significant difference was observed when assessed by a less experienced observer. Conclusions: Our findings suggest that Veriton-CT, with its full-ring CZT detector system, may offer advantages over fixed-angle gamma cameras in improving image quality and reducing attenuation artifacts in MPI. Although the difference in correlations with coronary angiography findings was not statistically significant, Veriton-CT showed a trend toward better agreement, particularly in the RCA basin. These results indicate that full-ring SPECT imaging could improve the diagnostic accuracy of non-invasive MPI, potentially reducing the need for unnecessary invasive angiography. Further studies with larger patient cohorts are required to confirm these findings and evaluate the clinical impact of full-ring SPECT technology in myocardial perfusion imaging. Full article

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model’s resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice. Full article

The $(k,\psi )$-fractional derivative based on the k-gamma function is a more general version of the Hilfer fractional derivative. It is widely used in differential equations to describe physical phenomena, population dynamics, and biological genetic memory problems. In this article, we mainly study the $4m+2$-point symmetric integral boundary value problem of nonlinear $(k,\psi )$-fractional differential coupled Laplacian equations. The existence and uniqueness of solutions are obtained by the Krasnosel’skii fixed-point theorem and Banach’s contraction mapping principle. Furthermore, we also apply the calculus inequality techniques to discuss the stability of this system. Finally, three interesting examples and numerical simulations are given to further verify the correctness and effectiveness of the conclusions. Full article

This paper presents the technical design of the pathfinder Barcelona Raman LIDAR (pBRL) for the northern site of the Cherenkov Telescope Array Observatory (CTAO-N) located at the Roque de los Muchachos Observatory (ORM). The pBRL is developed for continuous atmospheric characterization, essential for correcting high-energy gamma-ray observations captured by Imaging Atmospheric Cherenkov Telescopes (IACTs). The LIDAR consists of a steerable telescope with a 1.8 m parabolic mirror and a pulsed Nd:YAG laser with frequency doubling and tripling. It emits at wavelengths of 355 nm and 532 nm to measure aerosol scattering and extinction through two elastic and Raman channels. Built upon a former Cherenkov Light Ultraviolet Experiment (CLUE) telescope, the pBRL’s design includes a Newtonian mirror configuration, a coaxial laser beam, a near-range system, a liquid light guide and a custom-made polychromator. During a one-year test at the ORM, the stability of the LIDAR and semi-remote-controlled operations were tested. This pathfinder leads the way to designing a final version of a CTAO Raman LIDAR which will provide real-time atmospheric monitoring and, as such, ensure the necessary accuracy of scientific data collected by the CTAO-N telescope array. Full article

In this work, we analyze the vanishing cycles of Feynman loop integrals by the means of the Mayer–Vietoris spectral sequence. A complete classification of possible vanishing geometries is obtained. We use this result for establishing an asymptotic expansion for the loop integrals near their singularity locus and then give explicit formulas for the coefficients of such an expansion. Further development of this framework may potentially lead to exact calculations of one- and two-loop Feynman diagrams, as well as other next-to-leading and higher-order diagrams, in studies of radiative corrections for upcoming lepton–hadron scattering experiments. Full article

Skin diseases are listed among the most frequently encountered diseases. Skin diseases such as eczema, melanoma, and others necessitate early diagnosis to avoid further complications. This study aims to enhance the diagnosis of skin disease by utilizing advanced image processing techniques and an attention-based vision approach to support dermatologists in solving classification problems. Initially, the image is being passed through various processing steps to enhance the quality of the dataset. These steps are adaptive histogram equalization, binary cross-entropy with implicit averaging, gamma correction, and contrast stretching. Afterwards, enhanced images are passed through the attention-based approach for performing classification which is based on the encoder part of the transformers and multi-head attention. Extensive experimentation is performed to collect the various results on two publicly available datasets to show the robustness of the proposed approach. The evaluation of the proposed approach on two publicly available datasets shows competitive results as compared to a state-of-the-art approach. Full article