RSDC-Net: Robust self-supervised dynamic collaboration network for infrared and visible image fusion

Abstract

Infrared and visible image fusion (IVIF) aims to integrate complementary information from distinct sensors, yielding fused results that outperform the capabilities of either individual modality alone. Due to inherent modality bias, conventional fusion-reconstruction frameworks often struggle to effectively prioritize the representation of critical shared regions and diverse heterogeneous areas, while also showing deficiencies in shallow feature interactions. To address these challenges, we propose a robust self-supervised dynamic collaboration network (RSDC-Net), which adaptively and comprehensively selects complementary cues from both infrared and visible modalities. Specifically, we introduce a steady-state contrastive autoencoder that leverages a multi-task self-supervised strategy to enhance the robust representation of key shared cues in the mixed information flow. This strategy promotes deep cross-modal modeling of global dependencies across sources, thereby achieving semantic consistency. Furthermore, we design a latent inter-modal focus-guided module that integrates a bilateral transposed attention mechanism with a dynamic selection component to refine local-level heterogeneous cue allocation under the guidance of mutual global dependencies. Notably, a gated feed-forward unit is incorporated to filter outlier information flows across modalities. Quantitative results on the MSRS, TNO, and M3FD datasets demonstrate that RSDC-Net achieves the best performance on most of the eight evaluation metrics. Meanwhile, it also exhibits superior performance in qualitative visual assessments on these datasets as well as under challenging scenarios.