红外技术2024，Vol.46Issue(5)：510-521,12.
基于三分支对抗学习和补偿注意力的红外和可见光图像融合
Infrared and Visible Image Fusion Based on Three-branch Adversarial Learning and Compensation Attention Mechanism
摘要
Abstract
The existing deep learning image fusion methods rely on convolution to extract features and do not consider the global features of the source image.Moreover,the fusion results are prone to texture blurring,low contrast,etc.Therefore,this study proposes an infrared and visible image fusion method with adversarial learning and compensated attention.First,the generator network uses dense blocks and the compensated attention mechanism to construct three local-global branches to extract feature information.The compensated attention mechanism is then constructed using channel features and spatial feature variations to extract global information,infrared targets,and visible light detail representations.Subsequently,a focusing dual-adversarial discriminator is designed to determine the similarity distribution between the fusion result and source image.Finally,the public dataset TNO and RoadScene are selected for the experiments and compared with nine representative image fusion methods.The method proposed in this study not only obtains fusion results with clearer texture details and better contrast,but also outperforms other advanced methods in terms of the objective metrics.关键词
红外可见光图像融合/局部-全局三分支/局部特征提取/补偿注意力机制/对抗学习/聚焦双对抗鉴别器Key words
infrared-visible image fusion/local-global three-branch/local feature extraction/compensated attention mechanism/adversarial learning/focused dual adversarial discriminator分类
计算机与自动化引用本文复制引用
邸敬,任莉,刘冀钊,郭文庆,廉敬..基于三分支对抗学习和补偿注意力的红外和可见光图像融合[J].红外技术,2024,46(5):510-521,12.基金项目
国家自然科学基金(62061023) （62061023）
甘肃省杰出青年基金(21JR7RA345) （21JR7RA345）
甘肃省科技计划资助项目(22JR5RA360). （22JR5RA360）
