Infrared and visible image fusion aims to synergistically combine the thermal target saliency of infrared images with the rich textual details of visible images. To address the limitations of traditional multi-scale methods in terms of target-background contrast and detail preservation, this paper introduces a novel multi-scale pyramid cross-layer fusion framework. The core of this framework lies in a thermal expansion-based target separation mechanism for superior hierarchical decomposition. Source images are first decomposed via a Gaussian–Laplacian pyramid for multi-resolution representation. By exploiting infrared thermal saliency and visible geometric priors, the scene is explicitly segregated into a target layer and a background layer. The target layer employs deep feature extraction based on Iteratively Reweighted Nuclear Norm minimization to sharpen thermal prominences and enhance contrast; concurrently, the background layer undergoes a cross-modal, cross-layer consistency fusion strategy, integrating spatial textures across frequency bands to maintain structural fidelity and detail richness. This dual-layer paradigm, augmented by multi-scale aggregation, ensures seamless, artifact-free fusion. To comprehensively evaluate the proposed method, systematic experiments are conducted on two benchmark datasets: TNO and RoadScene. Evaluations on the dataset demonstrate that our method outperforms state-of-the-art baselines. Extended experiments on the MSRS dataset further confirm the strong generalization capability and robustness of our method. Furthermore, systematic hyperparameter experiments determine the optimal model configuration, and ablation studies substantiate the effective contribution of both the pyramid segregation module and the IRNN optimization module to the final fusion performance. Extensive hyperparameter testing identified the optimal setup, and ablation studies confirmed the contribution of each key module. Overall, our fusion algorithm demonstrates satisfactory performance in the experiments, representing a clear advance.
An et al. (Tue,) studied this question.