Image Details Research Articles

Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI.

The superficial white matter (SWM) consists of numerous short-range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly-curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra-high-resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high-resolution 0.76-mm isotropic acquisition using the generalized slice-dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25-mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP-YA) database. Our results demonstrate significant differences in the cortico-cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract-tracing literature. We find that the high-resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

Non-Surgical Endodontic Management of Teeth with Aberrant Anatomy: A Case Series

The root canal therapy aims to remove the necrotic and vital pulp tissue remnants from the root canal to treat the periradicular inflammation. The knowledge of the tooth anatomy is essential to ensure a successful treatment. However, the deviation of the tooth anatomy complicates the diagnostic and treatment processes. Effective management of such complexities requires a tailored approach, utilizing detailed imaging and precise endodontic techniques to achieve successful outcomes and restore patient function and comfort. This case series presents the endodontic management of three different anatomic variations of permanent teeth that include taurodontism in the mandibular molar, two rooted mandibular canine and dilacerated maxillary central incisor and underscores the importance of personalized treatment planning and advanced diagnostic tools in managing complex dental anomalies.

DWTN: deep wavelet transform network for lightweight single image deraining

On rainy days the uncertainty of the shape and distribution of rain streaks can cause the images captured by RGB image-based measurement tools to be blurred and distorted. Thanks to the wavelet transform ability to provide spatial and frequency domain information about an image and its multidirectional and multiscale nature, it is widely used in traditional image enhancement methods. In image deraining the distribution of rain streaks is not only related to spatial domain features but is also closely related to frequency domain spatial features. However, deep learning-based rain removal models mainly rely on the spatial features of the image, and RGB data can hardly distinguish rain marks from image details, which leads to the loss of crucial image information during rain removal. We have developed a lightweight single-image rain removal model called the deep wavelet transform network (DWTN) to address this limitation. This method separates image details from rain images and can more effectively remove rain marks. The proposed DWTN has three significant contributions. First, DWTN uses the feature components after the wavelet transform as the input to the model and assigns a separate frequency-aware enhancement block (FAEB) to each element. These blocks focus on specific frequency features that benefit the rain removal task. Second, we introduce a frequency feature fusion block (FFFB) that fuses different wavelet components to reduce noise and enhance the image background through a channel attention mechanism while attenuating rain streaks. Finally, we design a spatial feature enhancement block (SFEB), which uses a spatial attention mechanism to calibrate the spatial position of features to improve the rain removal performance. We evaluate the performance of DWTN using PSNR and SSIM on four synthetic datasets and NIQE and BRISQUE on two real datasets. The results of the evaluation of the six datasets and at least four performance metrics show that the proposed DWTN is superior to existing methods.

Unusual familial cystic kidney disease: combining fine radiologic and genetic evaluation to solve the case

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is the most prevalent hereditary kidney disease, characterized by enlarged kidneys with numerous cysts, high blood pressure, and a variety of extrarenal complications. This disease is a significant cause of renal failure and requires accurate differentiation from other cystic kidney diseases, especially when family history does not clearly indicate ADPKD. This is crucial due to differences in prognosis, treatment, and familial implications. Advanced molecular genetics and imaging techniques are employed to diagnose and assess the prognosis of patients and their families.Case presentationThe case study revolves around three patients from the same family—two sisters and one daughter—referred to a nephrology department for ADPKD management. The initial proband, a 42-year-old woman, experienced abdominal discomfort leading to an ultrasound that suggested ADPKD. However, MRI findings indicated standard-sized kidneys with bilateral parapelvic cysts, and no genetic markers for ADPKD were found. Her sister, presenting with controlled hypertension and similar ultrasound findings, also had her initial ADPKD diagnosis refuted by MRI and genetic testing, which revealed no significant mutations. The daughter, however, exhibited a different scenario with enlarged kidneys and multiple cysts characteristic of early-stage ADPKD. Genetic testing confirmed a deleterious PKD1 mutation, suggesting a de novo mutation, as her father showed no signs of the disease.ConclusionThis study highlights the complexity and necessity of thorough diagnostic processes in suspected ADPKD cases to prevent misdiagnosis. The initial symptoms and imaging might misleadingly suggest ADPKD, as seen in the cases of the two older patients. Still, further detailed imaging and genetic analyses revealed no ADPKD, preventing inappropriate treatment and stress. In contrast, the younger patient's distinctive clinical and genetic profile confirmed ADPKD, illustrating the variability within even closely related individuals. Such detailed assessments are crucial in guiding correct treatment decisions and providing accurate familial counseling, emphasizing the importance of considering a broader spectrum of renal cystic disorders before confirming a diagnosis of ADPKD.

Improved Brain Tumor Detection MRI Using Advanced Processing Techniques: Enhancement and Convolution Case Studies

Brain tumors present a significant challenge in medical imaging due to their complexity, requiring early detection and precise analysis for effective treatment. This study develops and evaluates advanced image processing workflows aimed at enhancing brain tumor image analysis. The proposed method involves four main steps: enlargement, pre-processing with min-max filters, enhancement, and convolution. The dataset used is from Kaggle, comprising 3,364 images categorized into Glioma (100 images), Meningioma (115 images), No Tumor (105 images), and Pituitary Tumor (74 images). For this study, images from the Glioma, Meningioma, and Pituitary Tumor categories were used, with one image selected from each category for technique evaluation. The results showed significant improvements in image clarity and detail, with high correlation values of 0.9851 for Meningioma and 0.9886 for Pituitary. These findings highlight the effectiveness of the proposed techniques in enhancing image quality and diagnostic accuracy.

Advanced Filtering and Enhancement Techniques for Diabetic Retinopathy Image Analysis

Diabetic retinopathy is a leading cause of visual impairment and blindness in diabetes sufferers. Early detection is crucial to prevent severe outcomes. This study presents an image processing method for retinal images to aid early detection. The method involves four steps: image enlargement, preprocessing, enhancement, and convolution. First, an algorithm enlarges the retinal image to increase resolution and reveal finer details. Preprocessing uses a min-max filtering algorithm to reduce noise and improve image quality. Next, specific pixel range enhancement techniques further refine the image and highlight relevant features. Finally, convolution with customized kernels detects and emphasizes areas indicating diabetic retinopathy, such as aneurysms and hemorrhages. Experimental results show improvement in image clarity and detail, enabling more accurate detection of diabetic retinopathy features. The correlation results are as follows: Filtering (0.35275, 0.20157, 0.4345), Enhancement (0.3214, 0.15823 0.34674), and Convolution (0.33542, 0.15758, 0.36826). The proposed algorithm enhances early detection and diagnosis by improving retinal image quality. Future work can optimize the algorithm and validate results with larger datasets, aiming to refine the determination of areas or pixel values relevant to diabetic retinopathy.

A fast all-optical 3D photoacoustic scanner for clinical vascular imaging.

The clinical assessment of microvascular pathologies (in diabetes and in inflammatory skin diseases, for example) requires the visualization of superficial vascular anatomy. Photoacoustic tomography (PAT) scanners based on an all-optical Fabry-Perot ultrasound sensor can provide highly detailed 3D microvascular images, but minutes-long acquisition times have precluded their clinical use. Here we show that scan times can be reduced to a few seconds and even hundreds of milliseconds by parallelizing the optical architecture of the sensor readout, by using excitation lasers with high pulse-repetition frequencies and by exploiting compressed sensing. A PAT scanner with such fast acquisition minimizes motion-related artefacts and allows for the volumetric visualization of individual arterioles, venules, venous valves and millimetre-scale arteries and veins to depths approaching 15 mm, as well as for dynamic 3D images of time-varying tissue perfusion and other haemodynamic events. In exploratory case studies, we used the scanner to visualize and quantify microvascular changes associated with peripheral vascular disease, skin inflammation and rheumatoid arthritis. Fast all-optical PAT may prove useful in cardiovascular medicine, oncology, dermatology and rheumatology.

Unraveling the enhanced sodium-storage mechanism in a strongly bonded 2D honeycomb borophene/boron phosphide heterostructure

The growing modern demand for battery capacity is driving the development of high-capacity metal-ion battery anodes for future energy storage. Two-dimensional (2D) material-based heterostructures have shown advantages as alternative anodes due to their enhanced adsorption capacity. The lightweight nature of honeycomb borophene (HB) is beneficial for serving as a high-capacity anode but is constrained by structural instability arising from electron deficiency. In this study, using first-principles calculations, we propose a HB/boron phosphide (BP) heterostructure as an anode for both lithium-ion batteries and sodium-ion batteries (SIBs). The heterostructure engineering not only stabilizes the HB structure but also leads to a bonding heterostructure instead of common van der Walls type. The HB/BP demonstrates robust structural stability and reversibility when multiple ions are stored. In addition, the HB/BP offers stable storage sites and low diffusion barriers for lithium (0.31 eV) and sodium (0.28 eV), indicating rapid charging–discharging performance. Notably, the predicted maximum sodium storage capacity reaches 2402 mAh/g, surpassing that of the constituent monolayers and most 2D heterostructures. The underlying mechanism for high storage capacity is elucidated through detailed charge image model analysis, offering atomistic-scale insights for constructing high-capacity anodes. All results suggest that the presented HB/BP is a promising anode candidate for SIBs and opens an avenue for stabilizing HB in energy storage.

Fast Sand-dust Image Enhancement Algorithm by Blue Channel Compensation and Guided Image Filtering

Computer vision systems are significantly impacted by the color variation and low visibility of images taken under sand-dust conditions To address the aforementioned issues, we provide a quick and efficient algorithm to improve photos taken under sand-dust situations. The loss value in the blue channel is first adjusted for. Subsequently, the image deteriorated by sand and dust is color corrected using white balance technology. Lastly, the picture contrast and edge correctness are improved using guided image filtering, and the image detail information is improved by using an adaptive method to determine the detail layer's magnification factor. The approach can successfully restore the fading features of sand-dust-deteriorated photos, according to the experimental results on a sizable number of degraded images by the dust in a brief amount of time, and enhance image sharpness. The suggested method may greatly improve the photos taken during sand-dust weather conditions, as shown by experimental findings via qualitative and quantitative assessments, and the outcomes are superior to those of other ways.

Comparative Analysis of Image Stitching Algorithms for Bridge Inspection Imaging Systems

This paper discusses an innovative vehicle-mounted device designed for efficient inspection of bridge undersides, aiming to address the challenges of manual inspections, such as being labor-intensive and inefficient. The device uses high-precision cameras to capture detailed images of bridge bottoms, facilitating a thorough condition assessment. It evaluates three feature point extraction algorithms for image stitching—Scale-Invariant Feature Transform (SIFT), Harris corner detection, and Speeded Up Robust Features (SURF)—to create complete visual representations of bridge undersides. Field tests on a prefabricated I girder bridge demonstrated the device's effectiveness in gathering and stitching images, with the SIFT algorithm performing best for girder bottoms, Harris corner algorithm for web and flange surfaces, and SURF for rapid image processing. This research provides a foundation for the rapid identification of visible defects on bridge superstructures, significantly benefiting bridge maintenance and safety evaluations.

Multi-Modal Fusion Network with Multi-Head Self-Attention for Injection Training Evaluation in Medical Education

The COVID-19 pandemic has significantly disrupted traditional medical training, particularly in critical areas such as the injection process, which require expert supervision. To address the challenges posed by reduced face-to-face interactions, this study introduces a multi-modal fusion network designed to evaluate the timing and motion aspects of the injection training process in medical education. The proposed framework integrates 3D reconstructed data and 2D images of hand movements during the injection process. The 3D data are preprocessed and encoded by a Long Short-Term Memory (LSTM) network to extract temporal features, while a Convolutional Neural Network (CNN) processes the 2D images to capture detailed image features. These encoded features are then fused and refined through a proposed multi-head self-attention module, which enhances the model’s ability to capture and weigh important temporal and image dynamics in the injection process. The final classification of the injection process is conducted by a classifier module. The model’s performance was rigorously evaluated using video data from 255 subjects with assessments made by professional physicians according to the Objective Structured Assessment of Technical Skill—Global Rating Score (OSATS-GRS)[B] criteria for time and motion evaluation. The experimental results demonstrate that the proposed data fusion model achieves an accuracy of 0.7238, an F1-score of 0.7060, a precision of 0.7339, a recall of 0.7238, and an AUC of 0.8343. These findings highlight the model’s potential as an effective tool for providing objective feedback in medical injection training, offering a scalable solution for the post-pandemic evolution of medical education.

High-definition multi-scale voice-driven facial animation: enhancing lip-sync clarity and image detail

High-definition multi-scale voice-driven facial animation: enhancing lip-sync clarity and image detail

Comparative Analysis of Impacted Mandibular Third Molar Root Proximity to the Mandibular Canal using Orthopantomography and Cone-beam Computed Tomography Imaging Modalities: A Pilot Study

Abstract Introduction: Fully detect risks of nerve damage, which can lead to temporary or permanent issues. Cone-beam computed tomography (CBCT) offers a three-dimensional (3D) view, providing more detailed visualisation of anatomical structures and their spatial relationships, which improves the accuracy of predicting nerve exposure. The study aims to evaluate and compare these imaging techniques’ effectiveness in categorising the relationship between third molars and the inferior alveolar canal, emphasising the importance of precise imaging for safer surgical outcomes. Materials and Methods: A pilot study involving 20 patients, representing 10% of the total sample size of 200, was conducted at Ahmedabad Dental College’s Department of Oral Medicine and Radiology. Investigators, trained to interpret radiological images from orthopantomography (OPG) and CBCT, compared their interpretations with those of two experts. A high inter-rater reliability was confirmed with a kappa statistic of 0.98. Following ethical approval, data were retrospectively collected from 20 cases, with digital OPG and CBCT images analysed and classified according to established criteria. Results: The results revealed a significant association between the results diagnosed through OPG and CBCT indicating similarity in their diagnosis. It was also seen that there was no bias towards the gender and the distribution was similar in case of diagnosis through OPG or CBCT. Conclusion: CBCT demands an in-depth understanding of anatomy and pathology, coupled with proficiency in operating imaging software and the ability to identify abnormalities in cross-sectional images. When executed and interpreted accurately, CBCT proves to be an exceptionally valuable tool in clinical dental practice. Its detailed 3D imaging capabilities enhance the assessment of complex cases, such as those involving intricate anatomical structures and pathologies. By providing comprehensive views that surpass traditional two-dimensional imaging, CBCT aids in precise diagnosis and treatment planning, making it an indispensable resource for addressing various dental conditions effectively.

Dual stage MRI image restoration based on blind spot denoising and hybrid attention

BackgroundMagnetic Resonance Imaging (MRI) is extensively utilized in clinical diagnostics and medical research, yet the imaging process is often compromised by noise interference. This noise arises from various sources, leading to a reduction in image quality and subsequently hindering the accurate interpretation of image details by clinicians. Traditional denoising methods typically assume that noise follows a Gaussian distribution, thereby neglecting the more complex noise types present in MRI images, such as Rician noise. As a result, denoising remains a challenging and practical task.MethodThe main research work of this paper focuses on modifying mask information based on a global mask mapper. The mask mapper samples all blind spot pixels on the denoised image and maps them to the same channel. By incorporating perceptual loss, it utilizes all available information to improve performance while avoiding identity mapping. During the denoising process, the model may mistakenly remove some useful information as noise, resulting in a loss of detail in the denoised image. To address this issue, we train a generative adversarial network (GAN) with adaptive hybrid attention to restore the detailed information in the denoised MRI images.ResultThe two-stage model NRAE shows an improvement of nearly 1.4 dB in PSNR and approximately 0.1 in SSIM on clinical datasets compared to other classic models. Specifically, compared to the baseline model, PSNR is increased by about 0.6 dB, and SSIM is only 0.015 lower. From a visual perspective, NRAE more effectively restores the details in the images, resulting in richer and clearer representation of image details.ConclusionWe have developed a deep learning-based two-stage model to address noise issues in medical MRI images. This method not only successfully reduces noise signals but also effectively restores anatomical details. The current results indicate that this is a promising approach. In future work, we plan to replace the current denoising network with more advanced models to further enhance performance.

Cardiac thrombus in acute ischemic stroke: impact on endovascular thrombectomy utilization

Cardiac embolism plays a very significant role in acute ischemic strokes (AIS), constituting approximately one-third of cases. The origin of these emboli often stems from intracardiac thrombi in the left atrium or left ventricle. Utilizing the National Readmission Database from 2016 to 2019, we investigate the prevalence of cardiac thrombi in AIS patients and explore their potential correlation with endovascular thrombectomy (EVT) utilization, and mortality rates. Among 1,272,456 AIS patients, 0.6% had concurrent cardiac thrombus, with an increasing trend observed over the study period (P value < 0.001). The cardiac thrombus cohort showed a higher prevalence of comorbidities such as congestive heart failure and atrial fibrillation. After propensity-score matching, groups were well-balanced in terms of baseline characteristics. Patients within the cardiac thrombus cohort experienced a longer hospital stay (median 5 days vs. 3 days), but no significant difference in mortality was noted. Importantly, the cardiac thrombus cohort demonstrated a higher frequency of EVT utilization, suggesting a link to larger vessel occlusions. Despite matching based on atrial fibrillation, the EVT utilization in the cardiac thrombus cohort remained high, highlighting a significant association. While 30-day readmission rates were comparable, cardiac thrombus patients faced a higher risk of gastrointestinal bleeding and hemorrhagic stroke, likely attributed to anticoagulation use. Limitations include potential miscoding in the administrative database and a lack of detailed imaging findings. In conclusion, this study highlights the increased likelihood of EVT in AIS patients with cardiac thrombus, possibly indicative of larger vessel occlusion associated with cardiac thrombus.

Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy

Light Sheet Fluorescence Microscopy (LSFM) is increasingly popular in neuroimaging for its ability to capture high-resolution 3D neural data. However, the presence of stripe noise significantly degrades image quality, particularly in complex 3D stripes with varying widths and brightness, posing challenges in neuroscience research. Existing stripe removal algorithms excel in suppressing noise and preserving details in 2D images with simple stripes but struggle with the complexity of 3D stripes. To address this, we propose a novel 3D U-net model for Stripe Removal in Light sheet fluorescence microscopy (USRL). This approach directly learns and removes stripes in 3D space across different scales, employing a dual-resolution strategy to effectively handle stripes of varying complexities. Additionally, we integrate a nonlinear mapping technique to normalize high dynamic range and unevenly distributed data before applying the stripe removal algorithm. We validate our method on diverse datasets, demonstrating substantial improvements in peak signal-to-noise ratio (PSNR) compared to existing algorithms. Moreover, our algorithm exhibits robust performance when applied to real LSFM data. Through extensive validation experiments, both on test sets and real-world data, our approach outperforms traditional methods, affirming its effectiveness in enhancing image quality. Furthermore, the adaptability of our algorithm extends beyond LSFM applications to encompass other imaging modalities. This versatility underscores its potential to enhance image usability across various research disciplines.

Research on Defect Detection Method of Fusion Reactor Vacuum Chamber Based on Photometric Stereo Vision.

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber.

Tooth Eruption Disorders: Literature Review and Clinical Case Reports

Introduction and purpose: Tooth eruption disorders are complex issues that can affect both primary and permanent teeth. The process of tooth eruption is a natural stage of development, but it can sometimes be disrupted. Common eruption disorders include delayed eruption, tooth impaction, premature eruption, and abnormalities in the location and direction of eruption. In each case, consultation with a specialist is required, along with early diagnosis and subsequent planning of appropriate treatment. State of knowledge: Impacted teeth are a common occurrence in dentistry and they occur in 0.8–3.6% of the general population The most frequently impacted teeth, in decreasing order of prevalence, are third molars, maxillary canines, mandibular premolars, and maxillary central incisors. Factors contributing to tooth impaction can be general, local, or genetic. Impacted teeth and difficulties with eruption may be associated with complications such as follicular cysts and a variant of these cysts known as eruption cysts. Summary: Tooth eruption disorders require an individualized diagnostic and therapeutic approach. Depending on the issue, treatment may involve observation, orthodontic interventions, and in some cases, surgical procedures. Early diagnosis and appropriate treatment are crucial to prevent complications such as malocclusion, damage to adjacent teeth, or unsatisfactory aesthetics. Radiographs can help confirm the presence of impacted teeth or in some cases, CBCT may be used for more detailed imaging.

Improving Reference-based Distinctive Image Captioning with Contrastive Rewards

Distinctive Image Captioning (DIC) — generating distinctive captions that describe the unique details of a target image — has received considerable attention over the last few years. A recent DIC method proposes to generate distinctive captions by comparing the target image with a set of semantic-similar reference images, i . e ., reference-based DIC (Ref-DIC). It aims to force the generated captions to distinguish between the target image and the reference image. Unfortunately, reference images used by existing Ref-DIC works are easy to distinguish: these reference images only resemble the target image at scene-level and have few common objects, such that a Ref-DIC model can trivially generate distinctive captions even without considering the reference images. For example, if the target image contains objects “ towel ” and “ toilet ” while all reference images are without them, then a simple caption “ A bathroom with a towel and a toilet ” is distinctive enough to tell apart target and reference images. To ensure Ref-DIC models really perceive the unique objects (or attributes) in target images, we first propose two new Ref-DIC benchmarks. Specifically, we design a two-stage matching mechanism, which strictly controls the similarity between the target and reference images at the object-/attribute- level (v.s. scene-level). Secondly, to generate distinctive captions, we develop a Transformer-based Ref-DIC baseline TransDIC . It not only extracts visual features from the target image, but also encodes the differences between objects in the target and reference images. Taking one step further, we propose a stronger TransDIC++ , which consists of an extra contrastive learning module to make full use of the reference images. This new module is model-agnostic, which can be easily incorporated into various Ref-DIC architectures. Finally, for more trustworthy benchmarking, we propose a new evaluation metric named DisCIDEr for Ref-DIC, which evaluates both the accuracy and distinctiveness of the generated captions. Experimental results demonstrate that our TransDIC++ can generate distinctive captions. Besides, it outperforms several state-of-the-art models on the two new benchmarks over different metrics.

SDAM: A dual attention mechanism for high-quality fusion of infrared and visible images.

Image fusion of infrared and visible images to obtain high-quality fusion images with prominent infrared targets has important applications in various engineering fields. However, current fusion processes encounter problems such as unclear texture details and imbalanced infrared targets and texture detailed information, which lead to information loss. To address these issues, this paper proposes a method for infrared and visible image fusion based on a specific dual-attention mechanism (SDAM). This method employs an end-to-end network structure, which includes the design of channel attention and spatial attention mechanisms. Through these mechanisms, the method can fully exploit the texture details in the visible images while preserving the salient information in the infrared images. Additionally, an optimized loss function is designed to combine content loss, edge loss, and structure loss to achieve better fusion effects. This approach can fully utilize the texture detailed information of visible images and prominent information in infrared images, while maintaining better brightness and contrast, which improves the visual effect of fusion images. Through conducted ablation experiments and comparative evaluations on public datasets, our research findings demonstrate that the SDAM method exhibits superior performance in both subjective and objective assessments compared to the current state-of-the-art fusion methods.