CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm
作者机构：Key Laboratory of Modern Power System Simulation and Control&Renewable Energy TechnologyMinistry of EducationSchool of Electrical EngineeringNortheast Electric Power UniversityJilin132012China School of Electrical EngineeringNortheast Electric Power UniversityJilin132012China School of Electronic Information EngineeringBozhou UniversityBozhou236800China
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第80卷第8期
页 面：2857-2872页
核心收录：
基 金：NIR
主 题：Image fusion deep learning auto-encoder(AE) infrared visible light
摘 要：To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is *** region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature *** the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image *** study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional *** methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other *** algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)*** experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.
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