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