Infrared–visible image fusion aims to integrate thermal target information from infrared images and structural texture information from visible images into a single informative image. Existing deep fusion methods still face challenges in preserving fine textures, maintaining structural consistency, and balancing complementary information under low-light conditions. To address these issues, this paper proposes MRCDFusion, an unsupervised infrared–visible image fusion network based on residual conditional diffusion and Transformer refinement. Specifically, a shared dense encoder is used to extract modality-specific and cross-modal complementary features from infrared and visible images. A Modality-Level Attention Module (MLAM) is then introduced to aggregate strong responses from infrared and visible features and construct modality-aware condition features for guiding the diffusion process. Instead of generating fused features from scratch, the proposed method adopts a base-plus-residual diffusion strategy, in which base features preserve global structures and residual diffusion enhances local details. A deterministic noise strategy is further introduced to improve inference reproducibility. The diffusion-enhanced features are refined by a window Transformer and depthwise separable convolutions, followed by gated feature fusion and progressive image reconstruction. Experiments are conducted primarily on the low-light LLVIP dataset, while FMB, TNO, and RoadScene are used for zero-shot cross-dataset evaluation without additional fine-tuning. The results show that MRCDFusion achieves particularly strong performance in gradient- and edge-related metrics while remaining competitive in visual information fidelity and cross-modal correlation metrics. Ablation studies verify the effectiveness of the main components, and downstream detection and auxiliary segmentation experiments further demonstrate the potential utility of the fused representations for subsequent visual perception tasks.

Infrared and visible image fusion aims to integrate the complementary information from heterogeneous sensors, thereby enhancing the robustness of visual perception in complex environments. Most existing methods, however, employ fixed network parameters, a characteristic that limits their adaptive modeling capabilities for cross-modal information under scenarios such as drastic illumination changes, low light conditions, dense fog, and strong glare. To address this issue, we propose a scene-adaptive image fusion network, termed HL-Fuse, based on dynamic low-rank modulation and frequency-domain collaboration. For the spatial domain, the Hyper-LoRA is introduced via our designed SceneHyperNet, which mathematically constrains parameter variations within a low-rank subspace to adaptively calibrate attention mappings according to the global scene information. For the frequency domain, a tailored FAM is introduced to bridge spatial-domain feature aggregation and explicit spectrum reweighting by implementing targeted high- and low-frequency filtering, thereby enhancing edge and texture representation. Experiments conducted on the MSRS, TNO, M³FD, and FMB datasets demonstrate that HL-Fuse achieves competitive performance in terms of both multiple objective metrics and subjective visual quality, while the overall performance in the MSRS downstream object detection task is also enhanced. These results indicate the potential value of HL-Fuse for complex scene perception and remote sensing applications.