首页|Multiscale feature learning and attention mechanism for infrared and visible image fusion

Multiscale feature learning and attention mechanism for infrared and visible image fusion

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Current fusion methods for infrared and visible images tend to extract features at a single scale,which results in insufficient detail and incomplete feature preservation.To address these issues,we propose an infrared and visible image fusion network based on a multiscale feature learning and attention mechanism(MsAFusion).A multiscale dilation convolution framework is employed to capture image features across various scales and broaden the perceptual scope.Furthermore,an attention network is introduced to enhance the focus on salient targets in infrared images and detailed textures in visible images.To compensate for information loss during convolution,jump connections are utilized during the image reconstruction phase.The fusion process utilizes a combined loss function consisting of pixel loss and gradient loss for unsupervised fusion of infraredand visible images.Extensive experiments on the dataset of electricity facilities demonstrate that our proposed method outperforms nine state-of-the-art methods in terms of visual perception and four objective evaluation metrics.

infrared and visible imagesimage fusionattention mechanismCNNfeature extraction

GAO Li、LUO DeLin、WANG Song

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School of Aerospace Engineering,Xiamen University,Xiamen 361102,China

Electric Power Research Institute,China Southern Power Grid,Guangzhou 510063,China

project of CSG Electric Power Research Institute

SEPRI-K22B100

2024

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子:1.056
ISSN:1674-7321
年,卷(期):2024.67(2)
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