MIFFuse: A Multi-Level Feature Fusion Network for Infrared and Visible Images

Abstract

Image fusion operation is beneficial to many applications and is also one of the most common and critical computer vision challenges. The perfect infrared and visible image fusion results should include the important infrared targets while preserving visible textural detail information as much as possible. A novel infrared and visible image fusion framework is proposed for this purpose. In this paper, the proposed fusion network (MIFFuse) is an end-to-end, multi-level-based fusion network for infrared and visible images. The presented approach makes effective use of the intermediate convolution layer’s output features to preserve the primary image fusion information. We also build a cat_block to swap information between two paths to gain more sufficient information during the convolution steps. To reduce the model’s running time even further, the proposed method that reduces the number of feature channels while maintaining the accuracy of the fusion performance. Extensive experiments on the TNO and CVC-14 image fusion datasets show that our MIFFuse outperforms the other methods in terms of both subjective visual effects and quantitative metrics. Furthermore, MIFFuse is approximately twice as fast as the most recent state-of-the-art methods. Our code and models can be found at https://github.com/depeng6/MIFFuse.

Keywords

• End-to-end framework
• multi-level features
• image fusion
• concatenation block