RALFusion: a residual attention guided lightweight deep-learning framework for infrared and visible image fusion

Infrared and visible image fusion plays an important role in subsequent advanced visual tasks, especially for complex marine environments. To improve the real-time performance of infrared and visible fusion tasks, a lightweight residual attention deep-learning method is introduced to address the challenges associated with the huge parameter quantities and long fusion time in fusion networks. Firstly, residual blocks are designed for global feature extraction, while channel attention is used to focus on local features to fully extract and distinguish infrared features and visible details. Secondly, RFB is introduced into the feature reconstruction module to improve the network reconstruction performance by expanding the receptive field. Finally, the simple and convenient addition fusion strategy is selected to ensure the lightness of the network. According to the experimental results, our method shows remarkable improvements in image fusion effect and fusion time consumption compared to other fusion methods.