This paper presents a novel Res2Net-based fusion framework for infrared and visible images. The proposed fusion model has three parts: an encoder, a fusion layer and a decoder, respectively. The Res2Net-based encoder is used to extract multi-scale features of source images, the paper introducing a new training strategy for training a Res2Net-based encoder that uses only a single image. Then, a new fusion strategy is developed based on the attention model. Finally, the fused image is reconstructed by the decoder. The proposed approach is also analyzed in detail. Experiments show that our method achieves state-of-the-art fusion performance in objective and subjective assessment by comparing with the existing methods.

翻译：本文为红外图像和可见图像提供了一个基于Res2Net的新型聚合框架。 拟议的聚合模型分为三部分: 编码器、 聚合层和解码器。 Res2Net的编码器用于提取源图像的多尺度特征, 该文件为培训仅使用单一图像的基于Res2Net的编码器引入了新的培训战略。 然后, 以关注模型为基础制定了一个新的聚合战略。 最后, 由解码器重建了融合图像。 也详细分析了拟议方法。 实验表明,我们的方法通过与现有方法进行比较,在客观和主观评估中取得了最先进的聚合性能。