HaarFuse: A dual-branch infrared and visible light image fusion network based on Haar wavelet transform

Infrared-visible image fusion remains challenging due to the inherent conflict between preserving multi-modal complementary features and minimizing reconstruction loss. Existing methods often suffer from inadequate feature representation and information degradation during fusion. To address this, we propose HaarFuse, a wavelet-enhanced auto-encoder network that hierarchically integrates multi-scale features for robust fusion. The network first employs wavelet transform to extend the receptive field of convolutional layers, extracting shared shallow features that encode both low-frequency structural contours and high-frequency texture primitives. Subsequently, the shallow features are decomposed into high-frequency and low-frequency components through Haar wavelet transform, and techniques such as INN, Gabor layer, and Transformer are adopted to further optimize and process these features. Finally, the fused image is reconstructed via the inverse wavelet transform. Experiments on TNO, MSRS, and M3FD benchmarks validate HaarFuse's superiority: it achieves the highest thermal saliency (SD=45.78, +5.5%↑ on MSRS; EN=6.98, +4.0%↑ on M3FD), optimal edge fidelity (Qabf=0.62, +1.6%↑ on M3FD), and 34.2 × faster inference than SwinFusion with 0.468MB parameters. Further validation in machine vision and medical imaging confirms its robustness for real-time applications.