DFG-HCEN: A distinctive-feature guided and hierarchical channel enhanced network-based infrared and visible image fusion

Abstract

In this paper, we propose an unsupervised learning approach for the task of infrared and visible image fusion. This approach is called a distinctive-feature guided and hierarchical channel enhanced network-based infrared and visible image Fusion (DFG-HCEN). Instead of using complex fusion rules, DFG-HCEN uses multi-level fusion to achieve fusion results, effectively avoiding information loss during feature extraction. To improve the fusion effect, we designed a distinctive-feature guided module that strengthens the relationship between modules. Moreover, the proposed hierarchical channel enhanced and distinctive-Feature guided module aims to facilitate the fusion framework in efficiently integrating the multilevel complementary features of the source pictures. In addition, we incorporate a hybrid loss method for unsupervised training of the provided DFG-HCEN. The fidelity loss is used to constrain the pixel similarity between the fused result and source images. The application of luminance regularization loss has been shown to be an efficient method for addressing the problem of luminance degradation in fused images. We conducted extensive experiments, including visual examination and quantitative analysis, comparing DFG-HCEN with thirteen other state-of-the-art fusion techniques. The results demonstrate the superiority of DFG-HCEN. Moreover, the extended object detection experiments validate the ability of DFG-HCEN to fully support downstream tasks.