BMFusion: Bridging the Gap Between Dark and Bright in Infrared-Visible Imaging Fusion

Abstract

The fusion of infrared and visible light images is a crucial technology for enhancing visual perception in complex environments. It plays a pivotal role in improving visual perception and subsequent performance in advanced visual tasks. However, due to the significant degradation of visible light image quality in low-light or nighttime scenes, most existing fusion methods often struggle to obtain sufficient texture details and salient features when processing such scenes. This can lead to a decrease in fusion quality. To address this issue, this article proposes a new image fusion method called BMFusion. Its aim is to significantly improve the quality of fused images in low-light or nighttime scenes and generate high-quality fused images around the clock. This article first designs a brightness attention module composed of brightness attention units. It extracts multimodal features by combining the SimAm attention mechanism with a Transformer architecture. Effective enhancement of brightness and features has been achieved, with gradual brightness attention performed during feature extraction. Secondly, a complementary fusion module was designed. This module deeply fuses infrared and visible light features to ensure the complementarity and enhancement of each modal feature during the fusion process, minimizing information loss to the greatest extent possible. In addition, a feature reconstruction network combining CLIP-guided semantic vectors and neighborhood attention enhancement was proposed in the feature reconstruction stage. It uses the KAN module to perform channel adaptive optimization on the reconstruction process, ensuring semantic consistency and detail integrity of the fused image during the reconstruction phase. The experimental results on a large number of public datasets demonstrate that the BMFusion method can generate fusion images with higher visual quality and richer details in night and low-light environments compared with various existing state-of-the-art (SOTA) algorithms. At the same time, the fusion image can significantly improve the performance of advanced visual tasks. This shows the great potential and application prospect of this method in the field of multimodal image fusion.