红外技术2024，Vol.46Issue(9)：1060-1069,10.
多尺度和卷积注意力相结合的红外与可见光图像融合
Infrared and Visible Image Fusion Combining Multi-scale and Convolutional Attention
摘要
Abstract
A multiscale and convolutional attention-based infrared and visible image fusion algorithm is proposed to address the issues of insufficient single-scale feature extraction and loss of details,such as infrared targets and visible textures,when fusing infrared and visible images.First,an encoder network,combining a multiscale feature extraction module and deformable convolutional attention module,is designed to extract important feature information of infrared and visible images from multiple receptive fields.Subsequently,a fusion strategy based on spatial and channel dual-attention mechanisms is adopted to further fuse the typical features of infrared and visible images.Finally,a decoder network composed of three convolutional layers is used to reconstruct the fused image.Additionally,hybrid loss function constraint network training based on mean squared error,multiscale structure similarity,and color is designed to further improve the similarity between the fused and source images.The results of the experiment are compared with seven image-fusion algorithms using a public dataset.In terms of subjective and objective evaluations,the proposed algorithm exhibits better edge preservation,source image information retention,and higher fusion image quality than other algorithms.关键词
红外与可见光图像/混合损失函数/多尺度特征提取/注意力机制/图像融合Key words
infrared and visible images/hybrid loss function/multi-scale feature extraction/attention mechanism/image fusion分类
计算机与自动化引用本文复制引用
祁艳杰,侯钦河..多尺度和卷积注意力相结合的红外与可见光图像融合[J].红外技术,2024,46(9):1060-1069,10.基金项目
山西省基础研究计划项目(202203021221144). （202203021221144）
