Lightweight Infrared and Visible Image Fusion Based on Attention Mechanism and Receptive Field Enhancement

To balance the accuracy and speed of image fusion, a lightweight infrared and visible image fusion algorithm is proposed in this paper. Firstly, to improve the feature extraction capability, ECA attention is introduced to enhance information exchange among channels. Secondly, to improve the feature reconstruction ability and lightweight network, RFB is used to enlarge the receptive field and minimize parameters. Finally, experimental results show that the proposed method outperforms other algorithms in quality and speed. The quantitative results demonstrate that our method effectively preserves infrared features and visible details, with minimal color distortion and uniform brightness. Among them, params decreased by 59.5%, and time shortened by 4.38%. Therefore, the proposed method has high engineering application potential.