Image Quality Enhancement Research Articles

Enhanced Wavelet-Based Medical Image Denoising with Bayesian-Optimized Bilateral Filtering.

Medical image denoising is essential for improving the clarity and accuracy of diagnostic images. In this paper, we present an enhanced wavelet-based method for medical image denoising, aiming to effectively remove noise while preserving critical image details. After applying wavelet denoising, a bilateral filter is utilized as a post-processing step to further enhance image quality by reducing noise while maintaining edge sharpness. The bilateral filter's effectiveness heavily depends on its parameters, which must be carefully optimized. To achieve this, we employ Bayesian optimization, a powerful technique that efficiently identifies the optimal filter parameters, ensuring the best balance between noise reduction and detail preservation. The experimental results demonstrate a significant improvement in image denoising performance, validating the effectiveness of our approach.

An optimized siamese neural network with deep linear graph attention model for gynaecological abdominal pelvic masses classification.

An adnexal mass, also known as a pelvic mass, is a growth that develops in or near the uterus, ovaries, fallopian tubes, and supporting tissues. For women suspected of having ovarian cancer, timely and accurate detection of a malignant pelvic mass is crucial for effective triage, referral, and follow-up therapy. While various deep learning techniques have been proposed for identifying pelvic masses, current methods are often not accurate enough and can be computationally intensive. To address these issues, this manuscript introduces an optimized Siamese circle-inspired neural network with deep linear graph attention (SCINN-DLGN) model designed for pelvic mass classification. The SCINN-DLGN model is intended to classify pelvic masses into three categories: benign, malignant, and healthy. Initially, real-time MRI pelvic mass images undergo pre-processing using semantic-aware structure-preserving median morpho-filtering to enhance image quality. Following this, the region of interest (ROI) within the pelvic mass images is segmented using an EfficientNet-based U-Net framework, which reduces noise and improves the accuracy of segmentation. The segmented images are then analysed using the SCINN-DLGN model, which extracts geometric features from the ROI. These features are classified into benign, malignant, or healthy categories using a deep clustering algorithm integrated into the linear graph attention model. The proposed system is implemented on a Python platform, and its performance is evaluated using real-time MRI pelvic mass datasets. The SCINN-DLGN model achieves an impressive 99.9% accuracy and 99.8% recall, demonstrating superior efficiency compared to existing methods and highlighting its potential for further advancement in the field.

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality. To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA. We enrolled 29 patients who underwent CT angiography before aortic repair. VMIs obtained at 70 and 40 keV were reconstructed using hybrid iterative reconstruction (HIR), and 40 keV VMIs were reconstructed using DLIR. The image noise of the spinal cord, the maximum CT values of the anterior spinal artery (ASA), and the contrast-to-noise ratio (CNR) of the ASA were compared. The overall image quality and the delineation of the AKA were evaluated on a 4-point score (1 = poor, 4 = excellent). The mean image noise of the spinal cord was significantly lower on 40-keV DLIR than on 40-keV HIR scans; they were significantly higher than on 70-keV HIR images. The CNR of the ASA was highest on the 40-keV DLIR images among the three reconstruction images. The mean image quality scores for 40-keV DLIR and 70-keV HIR scans were comparable, and higher than of 40-keV HIR images. The mean delineation scores for 40-keV HIR and 40-keV DLIR scans were significantly higher than for 70-keV HIR images. Visualization of the AKA was significantly better on low-keV VMIs subjected to DLIR than conventional HIR images.

An effectual underwater image enhancement framework using adaptive trans-resunet ++ with attention mechanism

ABSTRACT The intricacy of the underwater setting makes it difficult for optical lenses to capture clear underwater photos without haze and colour distortion. Some studies use domain adaptation and transfer learning to address this issue, they aim to reduce the latent mismatch between composition and real-world data, making the space of latent data difficult to read and impractical to control. The background light is a crucial component of the decaying paradigm that directly impacts how well images are enhanced. Thus, to improve the quality of the images over the underwater, new deep-learning techniques are being designed in this paper. Here, the Adaptive Trans-ResUnet++ with Attention Mechanism-based model performs the real-time underwater image enhancement process. In addition, a novel Random Enhanced Artificial Gorilla Troops Optimizer algorithm model is used for optimising the parameters over the given model to further enhance the given model’s performance. A diverse quantitative and qualitative validation is also carried out to learn the enhancement of underwater image quality. The enhanced underwater image may be also useful in the underwater object detection process. Thus, the enhanced images obtained from the developed model are compared with the existing techniques to confirm the efficacy of the suggested underwater image enhancement process.

Multiexposed Image-Fusion Strategy Using Mutual Image Translation Learning with Multiscale Surround Switching Maps

The dynamic range of an image represents the difference between its darkest and brightest areas, a crucial concept in digital image processing and computer vision. Despite display technology advancements, replicating the broad dynamic range of the human visual system remains challenging, necessitating high dynamic range (HDR) synthesis, combining multiple low dynamic range images captured at contrasting exposure levels to generate a single HDR image that integrates the optimal exposure regions. Recent deep learning advancements have introduced innovative approaches to HDR generation, with the cycle-consistent generative adversarial network (CycleGAN) gaining attention due to its robustness against domain shifts and ability to preserve content style while enhancing image quality. However, traditional CycleGAN methods often rely on unpaired datasets, limiting their capacity for detail preservation. This study proposes an improved model by incorporating a switching map (SMap) as an additional channel in the CycleGAN generator using paired datasets. The SMap focuses on essential regions, guiding weighted learning to minimize the loss of detail during synthesis. Using translated images to estimate the middle exposure integrates these images into HDR synthesis, reducing unnatural transitions and halo artifacts that could occur at boundaries between various exposures. The multilayered application of the retinex algorithm captures exposure variations, achieving natural and detailed tone mapping. The proposed mutual image translation module extends CycleGAN, demonstrating superior performance in multiexposure fusion and image translation, significantly enhancing HDR image quality. The image quality evaluation indices used are CPBDM, JNBM, LPC-SI, S3, JPEG_2000, and SSEQ, and the proposed model exhibits superior performance compared to existing methods, recording average scores of 0.6196, 15.4142, 0.9642, 0.2838, 80.239, and 25.054, respectively. Therefore, based on qualitative and quantitative results, this study demonstrates the superiority of the proposed model.

Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT

BackgroundThis study investigates the integration of Artificial Intelligence (AI) in compensating the lack of time-of-flight (TOF) of the GE Omni Legend PET/CT, which utilizes BGO scintillation crystals.MethodsThe current study evaluates the image quality of the GE Omni Legend PET/CT using a NEMA IQ phantom. It investigates the impact on imaging performance of various deep learning precision levels (low, medium, high) across different data acquisition durations. Quantitative analysis was performed using metrics such as contrast recovery coefficient (CRC), background variability (BV), and contrast to noise Ratio (CNR). Additionally, patient images reconstructed with various deep learning precision levels are presented to illustrate the impact on image quality.ResultsThe deep learning approach significantly reduced background variability, particularly for the smallest region of interest. We observed improvements in background variability of 11.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, 17.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, and 14.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} for low, medium, and high precision deep learning, respectively. The results also indicate a significant improvement in larger spheres when considering both background variability and contrast recovery coefficient. The high precision deep learning approach proved advantageous for short scans and exhibited potential in improving detectability of small lesions. The exemplary patient study shows that the noise was suppressed for all deep learning cases, but low precision deep learning also reduced the lesion contrast (about −30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}), while high precision deep learning increased the contrast (about 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}).ConclusionThis study conducted a thorough evaluation of deep learning algorithms in the GE Omni Legend PET/CT scanner, demonstrating that these methods enhance image quality, with notable improvements in CRC and CNR, thereby optimizing lesion detectability and offering opportunities to reduce image acquisition time.

Improving reverse time migration of ground-penetrating radar under zero-time imaging conditions using cross-correlation superposition projection

ABSTRACT Aiming to address the issue of significant artefact interference in conventional reverse time migration (RTM) imaging under zero-time imaging conditions of ground-penetrating radar (GPR), this study proposes an improvement of RTM using cross-correlation superposition projection (CSP) to enhance imaging quality. The method is based on the original RTM imaging framework, where all single-trace signals are individually processed using RTM. The individual imaging results are then multiplied pairwise and added together to create a CSP. After normalizing the CSP and properly setting the adaptive threshold, the CSP can focus RTM imaging on the GPR signal reflection interface area, effectively removing artefact interference. Synthetic data test confirms that the CSP-improved RTM, when compared to conventional RTM and Laplace filtering methods, not only preserves all effective information in imaging but also eliminates artefact interference. This improvement significantly enhances the quality and resolution of the imaging results. Furthermore, the practicality and effectiveness of the CSP-improved RTM method in engineering applications have been validated using a set of measured GPR data.

Deep Learning-Based Electric Field Enhancement Imaging Method for Brain Stroke

In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. However, these modalities are associated with potential hazards or limitations. In contrast, microwave imaging emerges as a promising technique, offering advantages such as non-ionizing radiation, low cost, lightweight, and portability. The primary challenges faced by microwave tomography include the severe ill-posedness of the electromagnetic inverse scattering problem and the time-consuming nature and unsatisfactory resolution of iterative quantitative algorithms. This paper proposes a learning electric field enhancement imaging method (LEFEIM) to achieve quantitative brain imaging based on a microwave tomography system. LEFEIM comprises two cascaded networks. The first, based on a convolutional neural network, utilizes the electric field from the receiving antenna to predict the electric field distribution within the imaging domain. The second network employs the electric field distribution as input to learn the dielectric constant distribution, thereby realizing quantitative brain imaging. Compared to the Born Iterative Method (BIM), LEFEIM significantly improves imaging time, while enhancing imaging quality and goodness-of-fit to a certain extent. Simultaneously, LEFEIM exhibits anti-noise capabilities.

Enhancing Semantic Segmentation in High-Resolution TEM Images: A Comparative Study of Batch Normalization and Instance Normalization

Abstract Integrating deep learning into image analysis for transmission electron microscopy (TEM) holds significant promise for advancing materials science and nanotechnology. Deep learning is able to enhance image quality, to automate feature detection, and to accelerate data analysis, addressing the complex nature of TEM datasets. This capability is crucial for precise and efficient characterization of details on the nano—and microscale, e.g., facilitating more accurate and high-throughput analysis of nanoparticle structures. This study investigates the influence of batch normalization (BN) and instance normalization (IN) on the performance of deep learning models for semantic segmentation of high-resolution TEM images. Using U-Net and ResNet architectures, we trained models on two different datasets. Our results demonstrate that IN consistently outperforms BN, yielding higher Dice scores and Intersection over Union metrics. These findings underscore the necessity of selecting appropriate normalization methods to maximize the performance of deep learning models applied to TEM images.

Complex-valued scatter compensation in nonlinear microscopy

Nonlinear, i.e., multiphoton microscopy is a powerful technique for imaging deep into biological tissues. Its penetration depth can be increased further using adaptive optics. In this work, we present a fast, feedback-based adaptive-optics algorithm, termed C-DASH, for multiphoton imaging through multiple-scattering media. C-DASH utilizes complex-valued light shaping (i.e., joint shaping of amplitude and phase), which offers several advantages over phase-only techniques: it converges faster, it delivers higher image quality enhancement, it shows a robust performance largely insensitive to the axial position of the correction plane, and it has a higher ability to cope with, on the one hand, scattering media that vary over time and, on the other hand, scattering media that are partially absorbing. Furthermore, our method is practically self-aligning. We also present a simple way to implement C-DASH using a single reflection off a phase-only spatial light modulator. We provide a thorough characterization of our method, presenting results of numerical simulations as well as two-photon excited fluorescence imaging experiments. Published by the American Physical Society 2024

Early Cervical Cancer Diagnosis with SWIN-Transformer and Convolutional Neural Networks.

Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train-test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models' effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage.

Enhancement of Image Quality in Low-Field Knee MR Imaging Using Deep Learning.

The purpose of this study is to investigate the potential of deep learning (DL) techniques to enhance the image quality of low-field knee MR images, with the ultimate goal of approximating the standards ofhigh-field knee MR imaging. We analyzed knee MR images collected from 45 patients with knee disorders and six normal subjects using a 3T MR scannerand those collected from 25 patients with knee disorders using a 0.4T MR scanner. Two DL models were developed: a fat-suppression contrast-generation model and a super-resolution model. These DL models were trained using 3T knee MR imaging data and applied to 0.4T knee MR imaging data. Visual assessments of anatomical structures and image noise and abnormality detection with diagnostic confidence levels on the original 0.4T MR images and those afterDL enhancement were conducted by two board-certified radiologists. Statistical analyses were performed using McNemar's test and the Wilcoxon signed-rank test. DL-enhanced MR images significantly improved the depiction of anatomical structures and reduced image noise compared to the original MR images. The number of abnormal findings detected and the diagnostic confidence levels were higher in the DL-enhanced MR images, indicating the potential for more accurate diagnoses. DL techniques effectively enhance the image quality of low-field knee MR images by leveraging 3T MR imaging data. This enhancement significantly improves image quality and diagnostic confidence levels, making low-field MR images much more reliable for detecting abnormalities. This advancement offers a useful alternative for clinical settings, especially in resource-limited environments, without compromising diagnostic accuracy.

An illumination-guided dual-domain network for image exposure correction

Exposure problems, including underexposure and overexposure, can significantly degrade image quality. Poorly exposed images often suffer from coupled illumination degradation and detail degradation, aggravating the difficulty of recovery. These necessitate a spatial discriminating exposure correction, making achieving uniformly exposed and visually consistent images challenging. To address these issues, we propose an Illumination-guided Dual-domain Network (IDNet), which employs a Dual-Domain Module (DDM) to simultaneously recover illumination and details from the frequency and spatial domains, respectively. The DDM also integrates a structural re-parameterization technique to enhance the detail-aware capabilities with reduced computational cost. An Illumination Mask Predictor (IMP) is introduced to guide exposure correction by estimating the optimal illumination mask. The comparison with 26 methods on three benchmark datasets shows that IDNet achieves superior performance with fewer parameters and lower computational complexity. These results confirm the effectiveness and efficiency of our approach in enhancing image quality across various exposure scenarios.

Comparison of Diffusion-weighted MRI using Singe-Shot Echo-planar Imaging (SS-EPI) and Split Acquisition of Fast Spin Echo Signal (SPLICE) Imaging, a non-EPI technique, in Tumors of the Head and Neck.

Diffusion-weighted imaging (DWI) using single-shot echo planar imaging (DW-EPI) is susceptible to distortions around air-filled cavities and dental fillings, typical for the head and neck area. Non-EPI, Split acquisition of fast spin echo signals for diffusion imaging (DWSPLICE) could reduce these distortions and enhance image quality, thereby potentially improving recurrence assessment in squamous cell carcinoma (SCC) of the head and neck region. This study evaluated whether DW-SPLICE is a viable alternative to DW-EPI through quantitative and qualitative analyses. The DW-SPLICE sequence was incorporated into the standard 3.0T head and neck MRI protocol with DW-EPI. Retrospective analysis was conducted on two subgroups: firstly benign or malignant lesions, and secondly post-treatment SCC recurrence. In both subgroups Image quality and distortion were scored by two independent radiologists, blinded for DW-technique, and evaluated using mixed-effect linear models. Lesion apparent diffusion coefficient (ADC) values were assessed with inter-class correlation (ICC) and Bland-Altman analyses. DWI's delineation geometric similarity to T1-weighted post-contrast (T1Wc) MRI was evaluated using the Dice Similarity Coefficient (DSC) before and after registration. Recurrence in post-treatment SCC scans was evaluated by the same two radiologists blinded for DW-technique. Recurrence detection rates were then compared between DW-SPLICE and DW-EPI using mixed logistic regression at six months and at one-year post-scan follow-up data. From August 2020 to January 2022, 55 benign or malignant lesion scans (55 patients) and 74 post-treatment SCC scans (66 patients) were analyzed. DW-SPLICE scored better on image quality and showed less overall distortion than DW-EPI (0.04<p<0.001). There was high ADC measurement reliability (ICC=0.93, p<0.001), though a proportional bias was also observed (β=0.11, p=0.03), indicating the bias increases as ADC values rise. DWSPLICE exhibited greater geometric similarity to T1Wc before registration (DSC 0.63 vs 0.47, p<0.001) and outperformed DW-EPI by more accurately identifying recurrences after one year (OR=0.96, p=0.05) but not after six months (OR=0.72, p=0.13). DW-SPLICE surpasses DW-EPI on image distortion and quality and improves diagnostic reliability for detecting recurrent or residual SCC on 3T MRI of the HN. Consistent use of one method for follow-up is advised, as ADC values are not completely interchangeable. Integrating DW-SPLICE can significantly improve tumor assessments in clinical practice. ANTs = Advanced Normalization Tools; DSC = Dice Similarity Coefficient; DW-EPI = Diffusion-weighted single-shot echo planar imaging; DW-MS-EPI = Diffusion-weighted multi-shot echo planar imaging; DW-SPLICE = Diffusion-weighted split acquisition of fast spin echo signals for diffusion imaging; DW-TSE = Diffusion-weighted Turbo Spin Echo; ICC = Intraclass Correlation Coefficient; ROC = Receiver Operating Characteristic; SCC = Squamous cell carcinoma; T1WIc = T1 weighted imaging with gadolinium contrast.

Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images

MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images.

Enhanced image quality and lesion detection in FLAIR MRI of white matter hyperintensity through deep learning-based reconstruction

ObjectiveTo delve deeper into the study of degenerative diseases, it becomes imperative to investigate whether deep-learning reconstruction (DLR) can improve the evaluation of white matter hyperintensity (WMH) on 3.0T scanners, and compare its lesion detection capabilities with conventional reconstruction (CR). MethodsA total of 131 participants (mean age, 46 years ±17; 46 men) were included in the study. The images of these participants were evaluated by readers blinded to clinical data. Two readers independently assessed subjective image indicators on a 4-point scale. The severity of WMH was assessed by four raters using the Fazekas scale. To evaluate the relative detection capabilities of each method, we employed the Wilcoxon signed rank test to compare scores between the DLR and the CR group. Additionally, we assessed interrater reliability using weighted k statistics and intraclass correlation coefficient to test consistency among the raters. ResultsIn terms of subjective image scoring, the DLR group exhibited significantly better scores compared to the CR group (P < 0.001). Regarding the severity of WMH, the DL group demonstrated superior performance in detecting lesions. Majority readers agreed that the DL group provided clearer visualization of the lesions compared to the conventional group. ConclusionDLR exhibits notable advantages over CR, including subjective image quality, lesion detection sensitivity, and inter reader reliability.

Self-correcting deep learning for estimating rice leaf nitrogen concentration with mobile phone images

As a pivotal role in crop development, the application of nitrogen fertilizers enhances yields, while excessive use poses environmental risks. Precision nitrogen management requires rapid crop nitrogen assessment in field conditions and mobile phone images can be a promising approach. However, few studies have investigated how arbitrary white balance and exposure value settings affect crop nitrogen diagnosis using phone photos. Therefore, a new deep learning framework: self-correcting leaf nitrogen concentration estimation network (SCLNC-Net), was proposed to explore the potential of mobile phone images for non-destructive nitrogen status monitoring. Imperfect camera metering and incorrect user operations were addressed through attention U-Net for automatic white balance and exposure correction. Then a lightweight deep learning algorithm MobileNetV3 was utilized to estimate rice leaf nitrogen concentration based on the self-corrected images. Compared with most deep learning models, SCLNC-Net has fewer parameters and higher computational efficiency, which is better suited for deployment on mobile platforms. The results revealed that SCLNC-Net enhanced image quality and acquired high accuracy in the estimation of rice leaf nitrogen concentration (R2 = 0.84, RMSE = 0.39 %, RRMSE = 13 %). Moreover, the robustness and effectiveness of the proposed SCLNC-Net remained excellent across various shooting angles, mobile phone types, and rice varieties. This study provides insights into rapid and cost-efficient nutrient assessment and precision nitrogen management.

PE Gas Pipeline Defect Detection Algorithm based on Improved YOLO v5

In order to improve defects detection efficiency in polyethylene (PE) gas pipelines and decrease leakage or other pipelines abnormalities in operation, this research proposed an improved YOLO(You Only Look Once) v5 detection model. First, the collected pipeline defect images were processed in grey scale, which improved the computational efficiency of the computer; then, Gamma transform and double filtering algorithms were applied respectively for image enhancement and noise reduction filtering of defects, which enhanced image quality and reduced image noise. Finally, the improved Sobel algorithm was applied to detect defective image edges and the defects in the image were segmented by adaptive threshold segmentation method to obtain binary images. The obtained binary images were employed to train the improved YOLO v5 detection model. The obtained experimental results showed that, compared with the original algorithm, the improved detection algorithm had better detection efficiency and higher robustness as well as higher recognition for common defects,improved YOLOv5 mAP and recall were 97.18% and 98.03%, respectively, the mAP has increased by 1.33% and the recall has increased by 3.83%,which can achieve the detection and identification of defect types of effects in PE gas pipes.

Charting Ethical Terrain: The Functon of Artificial Intelligence in Oral and Maxillofacial Imaging

The integration of artificial intelligence (AI) into maxillofacial imaging represents a significant advancement in diagnostics and therapy. This review explores the ethical implications of AI in this specialized area, addressing concerns such as data privacy, informed consent, and algorithmic bias. It highlights the potential benefits of AI for patient outcomes and clinical efficiency while acknowledging risks associated with reliance on automated systems. The review aims to establish a framework for ethical guidelines to ensure that AI enhances patient care. AI's application in various industries has gained momentum, with dentistry, particularly oral and maxillofacial radiology, emerging as a promising field. Recent studies have focused on convolutional neural networks for tasks such as image classification, detection, segmentation, and refinement. These AI systems support radiographic diagnosis, image analysis, forensic dentistry, and image quality enhancement. However, optimal performance requires large, well-labeled datasets, necessitating significant input from oral and maxillofacial radiologists, which can be time-intensive. For AI to be effectively integrated into clinical practice, several challenges must be overcome, including the creation of comprehensive open datasets, understanding AI judgment criteria, and addressing DICOM hacking threats. By developing solutions alongside AI advancements, the technology can significantly evolve, potentially transforming automated diagnosis, treatment planning, and tool development. Oral and maxillofacial radiologists will play a crucial role in shaping AI applications in their field, leveraging their expertise in interpreting radiographic images.

VSHARP: Variable Splitting Half-quadratic ADMM algorithm for reconstruction of inverse-problems

Medical Imaging (MI) tasks, such as accelerated parallel Magnetic Resonance Imaging (MRI), often involve reconstructing an image from noisy or incomplete measurements. This amounts to solving ill-posed inverse problems, where a satisfactory closed-form analytical solution is not available. Traditional methods such as Compressed Sensing (CS) in MRI reconstruction can be time-consuming or prone to obtaining low-fidelity images. Recently, a plethora of Deep Learning (DL) approaches have demonstrated superior performance in inverse-problem solving, surpassing conventional methods. In this study, we propose vSHARP (variable Splitting Half-quadratic ADMM algorithm for Reconstruction of inverse Problems), a novel DL-based method for solving ill-posed inverse problems arising in MI. vSHARP utilizes the Half-Quadratic Variable Splitting method and employs the Alternating Direction Method of Multipliers (ADMM) to unroll the optimization process. For data consistency, vSHARP unrolls a differentiable gradient descent process in the image domain, while a DL-based denoiser, such as a U-Net architecture, is applied to enhance image quality. vSHARP also employs a dilated-convolution DL-based model to predict the Lagrange multipliers for the ADMM initialization. We evaluate vSHARP on tasks of accelerated parallel MRI Reconstruction using two distinct datasets and on accelerated parallel dynamic MRI Reconstruction using another dataset. Our comparative analysis with state-of-the-art methods demonstrates the superior performance of vSHARP in these applications.