Abstract
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.
基金项目
project of CSG Electric Power Research Institute(SEPRI-K22B100)
维普
万方数据