Source Images Research Articles

3D modelling-based left atrium geometry analysis to predict atrial fibrillation in patients with cryptogenic stroke

Abstract Introduction Atrial fibrillation (AF) is a high prevalence condition associated with high morbidity and mortality. Its early diagnosis allows to initiate several prophylactic treatments that have proved to improve its prognosis. 3D modeling techniques allow to design accurate representations of cardiac cavities from diverse source images. Purpose We evaluated the association between new geometrical measurements obtained by 3D echocardiographic models of the left atrium (LA) and the presence of underlying paroxysmal AF in patients with cryptogenic stroke (CS). Methods 3D LA volumes from a sample of consecutive patients aged ≥60 years old and admitted to our Stroke Unit with the diagnosis of CS from April 2019 to November 2020 were analyzed. 15 days ECG monitoring was performed after discharge to detect underlying paroxysmal AF. All 3D echo volumes were analyzed by a 3D modeling open-source application. From this analysis several LA geometry parameters were obtained: sphericity, elongation, flatness, major axis length, minor axis length, maximum 2D diameter (max2Ddiam) and surface-volume ratio (S/V). Functional atrial parameters (3D left atrial ejection fraction, LAEF) were also determined. Results 36 patients were analyzed. Mean age was 78 ± 7 years old. 19% were diabetic, 58% had high blood pressure and 14% of patients had history of ischemic cardiomyopathy. Mean body-mass index was 26. These baseline characteristics were equally distributed between groups. Baseline proBNP was significantly higher in patients developing atrial fibrillation (949 vs 275 pg/mL; p= 0,014.). Maximum 2D diameter (max2Ddiam) and Surface-volume ratio (S/V) were the best predictors of AF (AUC of ROC curves: 0.69; 0.49 – 0.88 and 0.63; 0.42 – 0.84, respectively). An optimal cut-off point of 39 mm was selected for max2Ddiam and of 0.20 for S/V. These values were included in a multivariate model adjusted by baseline proBNP. A Max2Ddiam <39 mm and a S/V ≥0.20 were both significantly associated with the presence of paroxysmal AF (OR 6.96; p= 0.048 and OR 12.22; p = 0.043, respectively). Conclusions New geometrical atrial parameters can be obtained from echo 3D models and might be able to predict AF in patients with CS. Further analysis and larger samples are required to better understand these findings.TableFigure

M/EEG source localization for both subcortical and cortical sources using a convolutional neural network with a realistic head conductivity model

While electroencephalography (EEG) and magnetoencephalography (MEG) are well-established noninvasive methods in neuroscience and clinical medicine, they suffer from low spatial resolution. Electrophysiological source imaging (ESI) addresses this by noninvasively exploring the neuronal origins of M/EEG signals. Although subcortical structures are crucial to many brain functions and neuronal diseases, accurately localizing subcortical sources of M/EEG remains particularly challenging, and the feasibility is still a subject of debate. Traditional ESIs, which depend on explicitly defined regularization priors, have struggled to set optimal priors and accurately localize brain sources. To overcome this, we introduced a data-driven, deep learning-based ESI approach without the need for these priors. We proposed a four-layered convolutional neural network (4LCNN) designed to locate both subcortical and cortical sources underlying M/EEG signals. We also employed a sophisticated realistic head conductivity model using the state-of-the-art segmentation method of ten different head tissues from individual MRI data to generate realistic training data. This is the first attempt at deep learning-based ESI targeting subcortical regions. Our method showed excellent accuracy in source localization, particularly in subcortical areas compared to other methods. This was validated through M/EEG simulations, evoked responses, and invasive recordings. The potential for accurate source localization of the 4LCNNs demonstrated in this study suggests future contributions to various research endeavors such as the clinical diagnosis, understanding of the pathophysiology of various neuronal diseases, and basic brain functions.

Quantitative EEG biomarkers for STXBP1-related disorders.

EEG patterns and quantitative EEG (qEEG) features have been poorly explored in monogenic epilepsies. Herein, we investigate regional differences in EEG frequency composition in patients with STXBP1 developmental and epileptic encephalopathy (STXBP1-DEE). We conducted a retrospective study collecting electroclinical data of patients with STXBP1-DEE and two control groups of patients with DEEs of different etiologies and typically developing individuals matched for age and sex. We performed a (1) visual EEG assessment, (b) qEEG analysis, and (c) electrical source imaging (ESI). We quantified the relative power (RP) of four frequency bands (α β, θ, δ), in two electrode groups (anterior/posterior), and compared their averages and dynamics (standard deviation [SD] over time). The ESI was performed by applying the standard Distributed Source Modeling algorithm. We analyzed 42 EEG studies in 19 patients with STXBP1-DEE (10 female), with a median age at recordings of 9.6 years (range 9 months to 29 years). The δRP was higher in recordings of STXBP1-DEE (p < .001) compared to both control groups, suggesting the pathogenicity and STXBP1-specificity of these findings. In STXBP1-DEE, the δRP was significantly higher in the anterior electrode group compared to the posterior one (p = .003). There was no correlation between the anterior δRP and the epilepsy focus, age at recordings, and concomitant medications The ESI modeling of this activity showed a widespread involvement of the dorsomesial frontal cortex, suggesting a large corticosubcortical pathologic network. Finally, we identified two groups of recordings: cluster.1 with higher anterior δRP and low dynamics and cluster.2 with lower δRP and higher dynamics. Patients in cluster.1 had a more severe epilepsy and neurological phenotype compared to patients in cluster 2. The qEEG analysis showed a predominant frontal slow activity as a specific STXBP1 feature that correlates with the severity of the phenotype and may represent a biomarker for prospective longitudinal studies of STXBP1-DEE.

Blood-brain barrier profile pretreatment is associated with hemorrhagic transformation after endovascular reperfusion.

While advances in endovascular thrombectomy (EVT) have led to high reperfusion rates, most patients treated with EVT do not avoid disability. Post-reperfusion hemorrhagic transformation (HT) is a potential target for improving outcomes. This study examined pretreatment blood-brain barrier (BBB) disruption in tissue that would subsequently become part of the final infarct to evaluate its role in post-EVT HT. This post hoc analysis of the FRAME study, which enrolled patients with anterior large vessel occlusion who received EVT within 6 hours of onset, included patients if they had successful pretreatment MRI perfusion weighted imaging (PWI) and underwent successful EVT. BBB disruption was measured as the percent signal change due to gadolinium leakage on the PWI source images prior to thrombectomy. Mean permeability derangement (MPD) was defined as the average of all voxels in the stroke core that are two standard deviations above normal. The primary outcome was hemorrhagic transformation with parenchymal hematoma (PH). In total, 164 patients were included; mean age was 71 and 48% were female. PH occurred in 57 patients. Median MPD was 13.5% for patients with PH versus 3.6% for patients without (p < 0.0001). Elevated MPD was independently associated with PH with a 20% increased risk of PH for each 5% increase in MPD (OR 1.206; 95% CI 1.037:1.405; p = 0.0147, adjusted for NIHSS and procedure duration). Even in patients who are successfully recanalized in an early time window, pretreatment BBB disruption in regions that go on to infarct is associated with an increased risk of post-EVT HT.

Multimodal and quantitative analysis of the epileptogenic zone network in the pre-surgical evaluation of drug-resistant focal epilepsy

Surgical resection for epilepsy often fails due to incomplete Epileptogenic Zone Network (EZN) localization from scalp electroencephalography (EEG), stereo-EEG (SEEG), and Magnetic Resonance Imaging (MRI). Subjective interpretation based on interictal, or ictal recordings limits conventional EZN localization. This study employs multimodal analysis using high-density-EEG (HDEEG), Magnetoencephalography (MEG), functional-MRI (fMRI), and SEEG to overcome these limitations in a patient with drug-resistant MRI-negative focal epilepsy. A 17-year-old with drug-resistant epilepsy underwent evaluation. HDEEG, MEG, fMRI, and SEEG were used, with a novel HDEEG-cap facilitating simultaneous EEG-MEG and EEG-fMRI recordings. Electrical and magnetic source imaging were performed, and fMRI data were analysed for homogenous regions. SEEG analysis involved spike detection, spike timing analysis, ictal fast activity quantification, and Granger-based connectivity analysis. Non-invasive sessions revealed consistent interictal source imaging results identifying the EZN in the right anterior cingulate cortex. EEG-fMRI highlighted broader activation in the right cingulate cortex. SEEG analysis localized spikes and fast activity in the right anterior and posterior cingulate gyri. Multi-modal analysis suggested the EZN in the right frontal lobe, primarily involving the anterior and mid-cingulate cortices. Multi-modal non-invasive analyses can optimise SEEG implantation and surgical decision-making. Invasive analyses corroborated non-invasive findings, emphasising the importance of individual-case quantitative analysis across modalities in complex epilepsy cases.

Big data research is everyone's research-Making epilepsy data science accessible to the global community: Report of the ILAE big data commission.

Epilepsy care generates multiple sources of high-dimensional data, including clinical, imaging, electroencephalographic, genomic, and neuropsychological information, that are collected routinely to establish the diagnosis and guide management. Thanks to high-performance computing, sophisticated graphics processing units, and advanced analytics, we are now on the cusp of being able to use these data to significantly improve individualized care for people with epilepsy. Despite this, many clinicians, health care providers, and people with epilepsy are apprehensive about implementing Big Data and accompanying technologies such as artificial intelligence (AI). Practical, ethical, privacy, and climate issues represent real and enduring concerns that have yet to be completely resolved. Similarly, Big Data and AI-related biases have the potential to exacerbate local and global disparities. These are highly germane concerns to the field of epilepsy, given its high burden in developing nations and areas of socioeconomic deprivation. This educational paper from the International League Against Epilepsy's (ILAE) Big Data Commission aims to help clinicians caring for people with epilepsy become familiar with how Big Data is collected and processed, how they are applied to studies using AI, and outline the immense potential positive impact Big Data can have on diagnosis and management.

An ensemble deep learning model for medical image fusion with Siamese neural networks and VGG-19.

Multimodal medical image fusion methods, which combine complementary information from many multi-modality medical images, are among the most important and practical approaches in numerous clinical applications. Various conventional image fusion techniques have been developed for multimodality image fusion. Complex procedures for weight map computing, fixed fusion strategy and lack of contextual understanding remain difficult in conventional and machine learning approaches, usually resulting in artefacts that degrade the image quality. This work proposes an efficient hybrid learning model for medical image fusion using pre-trained and non-pre-trained networks i.e. VGG-19 and SNN with stacking ensemble method. The model leveraging the unique capabilities of each architecture, can effectively preserve the detailed information with high visual quality, for numerous combinations of image modalities in image fusion challenges, notably improved contrast, increased resolution, and lower artefacts. Additionally, this ensemble model can be more robust in the fusion of various combinations of source images that are publicly available from Havard-Medical-Image-Fusion Datasets, GitHub. and Kaggle. Our proposed model performance is superior in terms of visual quality and performance metrics to that of the existing fusion methods in literature like PCA+DTCWT, NSCT, DWT, DTCWT+NSCT, GADCT, CNN and VGG-19.

Unsupervised generative model for simulating post-operative double eyelid image.

Simulating the outcome of double eyelid surgery is a challenging task. Many existing approaches rely on complex and time-consuming 3D digital models to reconstruct facial features for simulating facial plastic surgery outcomes. Some recent research performed a simple affine transformation approach based on 2D images to simulate double eyelid surgery outcomes. However, these methods have faced challenges, such as generating unnatural simulation outcomes and requiring manual removal of masks from images. To address these issues, we have pioneered the use of an unsupervised generative model to generate post-operative double eyelid images. Firstly, we created a dataset involving pre- and post-operative 2D images of double eyelid surgery. Secondly, we proposed a novel attention-class activation map module, which was embedded in a generative adversarial model to facilitate translating a single eyelid image to a double eyelid image. This innovative module enables the generator to selectively focus on the eyelid region that differentiates between the source and target domain, while enhancing the discriminator's ability to discern differences between real and generated images. Finally, we have adjusted the adversarial consistency loss to guide the generator in preserving essential features from the source image and eliminating any masks when generating the double eyelid image. Experimental results have demonstrated the superiority of our approach over existing state-of-the-art techniques.

Learning infrared degradations for coherent visible image fusion in the undecimated dual-tree complex wavelet domain

Traditional infrared (IR) and visible (VIS) image fusion methods demand identical resolution levels for source images, which can be problematic due to the inherent low-resolution nature of IR imagery. In this paper, we introduce an innovative image fusion approach that harmonizes resolution across IR-VIS source images, leading to the generation of fused images with higher resolution. We employ a convolutional neural network model to recover the real-time image degradations in IR data, with a particular focus on super-resolution through the multi degradation resolution enhancement network (MDREN). We adopt undecimated dual-tree complex wavelet transform (UDT-CWT) in our fusion process due to its near shift invariance and better directionality capabilities. This results in coherent information of the fused images with minimized noise and loss. Experiments employing five image quality assessment measures are used to compare the proposed method to nine state-of-the-art approaches and show its efficacy.

Brain Tumor Detection through Image Fusion Using Cross Guided Filter and Convolutional Neural Network

This Data fusion has become a significant issue in diagnostic imaging, particularly in medical applications like radiation and guided image surgery. Medical image fusion aims to enhance the precision of tumor diagnosis, by preserving the salient information and characteristics of the original images in the fused image. It has been shown that guided filters are capable of maintaining edges well. In this paper, we propose a novel cross-guided filter-based fusion approach for multimodal medical images utilizing convolutional neural networks. The cross-guided filter is used in the proposed algorithm to extract the detailed features from the source images. Convolutional neural networks are used to generate the feature weights of source images derived from the detail layers. The weighted average rule is used to merge the source images based on these weights. We used thirty distinct types of medical images from diverse sources to compare the effectiveness of the proposed strategy to that of existing methods, both numerically and visually. The experimental findings demonstrated that, in terms of both objective evaluation and qualitative image quality, the suggested system performs better than other standard methods already in use. The quantitative results show that compared to existing methods under consideration for comparison, the proposed algorithm improves mutual information by 25%, image entropy by 9.5%, spatial frequency by 21%, standard deviation by 18.1%, structural similarity index by 30%, and edge strength of the fused image by 39%.

ESFuse: Weak Edge Structure Perception Network for Infrared and Visible Image Fusion

Infrared and visible image fusion (IVIF) fully integrates the complementary features of different modal images, and the fused image provides a more comprehensive and objective interpretation of the scene compared to each source image, thus attracting extensive attention in the field of computer vision in recent years. However, current fusion methods usually center their attention on the extraction of prominent features, falling short of adequately safeguarding subtle and diminutive structures. To address this problem, we propose an end-to-end unsupervised IVIF method (ESFuse), which effectively enhances fine edges and small structures. In particular, we introduce a two-branch head interpreter to extract features from source images of different modalities. Subsequently, these features are fed into the edge refinement module with the detail injection module (DIM) to obtain the edge detection results of the source image, improving the network’s ability to capture and retain complex details as well as global information. Finally, we implemented a multiscale feature reconstruction module to obtain the final fusion results by combining the output of the DIM with the output of the head interpreter. Extensive IVIF fusion experiments on existing publicly available datasets show that the proposed ESFuse outperforms the state-of-the-art(SOTA) methods in both subjective vision and objective evaluation, and our fusion results perform well in semantic segmentation, target detection, pose estimation and depth estimation tasks. The source code has been availabled.

Seeing Beyond Crisis: Analyzing Photographs and Photographer Bylines in Solutions-Oriented Environmental Journalism Stories

ABSTRACT Environmental journalism helps readers better understand the relationship between humanity and humanity’s home, but visual aspects of this type of reporting often focus more on conflict and distant human suffering. Though this is somewhat changing in print media, there remains a lack of focus on the action-forward type of reporting that might mobilize and educate readers toward what can be done about environmental problems. This study looks at environmental journalism visuals through a solutions journalism perspective, which compels journalists to add to their coverage of problems by asking and showing what people are doing about it. Through a content analysis, we explore how the environment is visually represented in solutions-oriented news stories. We found key differences based on story topic, as well as how people were included and depicted within the images. We also discuss the significant reliance on outside sources for images used in these stories.

Key Technologies and Typical Applications in Deepfake Detection

As deepfaking technology advances, it increasingly facilitates the seamless splicing of a person's voice, facial expressions, and body movements into source images or videos to create hyper-realistic virtual content. This growing technological sophistication, paired with the open-source nature of neural network algorithms, has unfortunately led to the exploitation of deepfake technology by individuals for illicit activities, seeking financial gain. This situation highlights the urgent necessity to thoroughly understand and vigilantly monitor the evolution of deepfake technology. This paper delves into the fundamental concepts and operational mechanisms underlying contemporary deepfake detection methods. It aims to augment the comprehension of these technologies and assesses the strengths and weaknesses of current detection approaches. This evaluation provides critical insights into their operational efficacy and reliability in differentiating genuine from manipulated content. Furthermore, the paper explores the challenges that current technologies face, such as the rapid evolution of deepfake methods that may outpace detection capabilities. By thoroughly analyzing these technologies, the paper endeavors to offer a comprehensive overview and projects future trends in deepfake detection. It anticipates developments that could significantly enhance the efficacy of countermeasures and thus bolster the fight against digital identity fraud and misinformation, safeguarding digital media integrity in an increasingly synthetic landscape.

Medical Image Fusion for Multiple Diseases Features Enhancement

ABSTRACTThroughout the past 20 years, medical imaging has found extensive application in clinical diagnosis. Doctors may find it difficult to diagnose diseases using only one imaging modality. The main objective of multimodal medical image fusion (MMIF) is to improve both the accuracy and quality of clinical assessments by extracting structural and spectral information from source images. This study proposes a novel MMIF method to assist doctors and postoperations such as image segmentation, classification, and further surgical procedures. Initially, the intensity‐hue‐saturation (IHS) model is utilized to decompose the positron emission tomography (PET)/single photon emission computed tomography (SPECT) image, followed by a hue‐angle mapping method to discriminate high‐ and low‐activity regions in the PET images. Then, a proposed structure feature adjustment (SFA) mechanism is used as a fusion strategy for high‐ and low‐activity regions to obtain structural and anatomical details with minimum color distortion. In the second step, a new multi‐discriminator generative adversarial network (MDcGAN) approach is proposed for obtaining the final fused image. The qualitative and quantitative results demonstrate that the proposed method is superior to existing MMIF methods in preserving the structural, anatomical, and functional details of the PET/SPECT images. Through our assessment, involving visual analysis and subsequent verification using statistical metrics, it becomes evident that color changes contribute substantial visual information to the fusion of PET and MR images. The quantitative outcomes demonstrate that, in the majority of cases, the proposed algorithm consistently outperformed other methods. Yet, in a few instances, it achieved the second‐highest results. The validity of the proposed method was confirmed using diverse modalities, encompassing a total of 1012 image pairs.

Non-commutative Schwarzschild black hole surrounded by Perfect fluid dark matter: Plasma lensing and thermodynamics analysis

The focus of this paper is to examine the properties of thermodynamics and weak gravitational lensing about the geometry of black holes within the context of a non-commutative Schwarzschild black hole surrounded by Perfect fluid dark matter. We examine the geometric mass and thermal temperature in this context to discuss the stability of the black hole solution. We examine the phase transition and stability while calculating the specific heat. We also research the black hole's energy emission process. We deduce that our researched black hole solution is thermally stable based on its thermodynamic features. Furthermore, we analyze uniform and non-uniform plasma by calculating the deflection angle, and we examine gravitational lensing in the weak plasma field. It is observed that in uniform plasma, the deflection angle is larger than in non-uniform plasma. We also looked at the image magnification caused by source brightness and found that the source image is enlarged more in uniform plasma than in non-uniform plasma.

Caries Detection and Classification in Photographs Using an Artificial Intelligence-Based Model-An External Validation Study.

This ex vivo diagnostic study aimed to externally validate a freely accessible AI-based model for caries detection, classification, localisation and segmentation using an independent image dataset. It was hypothesised that there would be no difference in diagnostic performance compared to previously published internal validation data. For the independent dataset, 718 dental images representing different stages of carious (n = 535) and noncarious teeth (n = 183) were retrieved from the internet. All photographs were evaluated by the dental team (reference standard) and the AI-based model (test method). Diagnostic performance was statistically determined using cross-tabulations to calculate accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC). An overall ACC of 92.0% was achieved for caries detection, with an ACC of 85.5-95.6%, SE of 42.9-93.3%, SP of 82.1-99.4% and AUC of 0.702-0.909 for the classification of caries. Furthermore, 97.0% of the cases were accurately localised. Fully and partially correct segmentation was achieved in 52.9% and 44.1% of the cases, respectively. The validated AI-based model showed promising diagnostic performance in detecting and classifying caries using an independent image dataset. Future studies are needed to investigate the validity, reliability and practicability of AI-based models using dental photographs from different image sources and/or patient groups.

Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain

The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image.

Palette‐based colour normalization for histopathology images

AbstractWith the emergence of computer‐aided diagnostic (CAD) systems, the speed of histopathological image analysis and the accuracy of cancer detection have considerably improved. However, the appearance of histopathological slides can vary depending on the consistency of tissue thickness, stain concentrations, and equipment, thus affecting the judgment of CAD systems. This study proposes a palette‐based colour normalization method for histopathology images is proposed to solve the problem of colour differences between histopathology images. The method builds a graphical user interface based on an improved palette generation algorithm and colour transfer definitions, through which the user can complete the corresponding colour modification to achieve colour normalization. The evaluation of the metrics on histopathological images shows that our method is able to achieve higher image quality and better preservation of structural information of the source image compared with four other colour normalization algorithms. The peak signal‐to‐noise ratio values obtained by the proposed method on two publicly available datasets were 24.1914 and 21.3666, and the structural similarity index matrix values were 0.9871 and 0.9760. The proposed method provides new ideas for the development and design of CAD systems.

Electroencephalographic source imaging of spikes with concurrent high-frequency oscillations is concordant with the clinical ground truth.

Epilepsy raises critical challenges to accurately localize the epileptogenic zone (EZ) to guide presurgical planning. Previous research has suggested that interictal spikes overlapping with high-frequency oscillations, referred to here as pSpikes, serve as a reliable biomarker for EZ estimation, but there remains a question as to whether and to how pSpikes perform as compared to other types of epileptic spikes. This study aims to address this question by investigating the source imaging capabilities of pSpikes alongside other spike types. A total of 2819 interictal spikes from 76-channel scalp electroencephalography (EEG) were analyzed in a cohort of 24 drug-resistant focal epilepsy patients. All patients received surgical resection, and 16 were declared seizure-free based on at least 1 year of postoperative follow-up. A recently developed electrophysiological source imaging algorithm-fast spatiotemporal iteratively reweighted edge sparsity (FAST-IRES)-was used for source imaging of the detected interictal spikes. The performance of 217 pSpikes was compared with 772 nSpikes (spikes with irregular high-frequency activations), 1830 rSpikes (spikes with no high-frequency activity), and all 2819 aSpikes (all interictal spikes). The localization and extent estimation using pSpikes are concordant with the clinical ground truth; using pSpikes yields the best performance compared with nSpikes, rSpikes, and conventional spike imaging (aSpikes). For multiple spike type seizure-free patients, the mean localization error for pSpike imaging was 6.8 mm, compared with 15.0 mm for aSpikes. The sensitivity, precision, and specificity were .41, .67, and .93 for pSpikes compared with .32, .48, and .93 for aSpikes. These results demonstrate the merits of noninvasive EEG source localization, and that (1) pSpike is a superior biomarker, outperforming conventional spike imaging for the localization of epileptic sources, and especially those with multiple irritative zones; and (2) FAST-IRES provides accurate source estimation that is highly concordant with clinical ground truth, even insituations of single spike analysis with low signal-to-noise ratio.

Denoiser Learning for Infrared and Visible Image Fusion.

Infrared image (IR) and visible image (VI) fusion creates fusion images that contain richer information and gain improved visual effects. Existing methods generally use the operators of manual design, such as intensity and gradient operators, to mine the image information. However, it is hard for them to achieve a complete and accurate description of information, which limits the image fusion performance. To this end, a novel information measurement method is proposed to achieve IR and VI fusion. Its core idea is to guide a generator in achieving image fusion by learning the denoisers. Specifically, by using denoisers to restore fusion images with different noise interference to source images, a mutual competition relationship is formed between denoisers, which helps the generator thoroughly explore the data specificity of the source images and guide it to achieve more accurate feature representation. In addition, a semantic adaptive measurement loss function is proposed to constrain the generator, which fuses semantic information adaptively by considering the semantic information density of different source images. The results of quantitative and qualitative experiments have shown that the proposed method can achieve a higher quality information fusion and has a faster fusion speed on three public datasets when compared with advanced methods.