Hybrid Fusion: Minute-Scale Training for Zero-Shot Cross-Domain Image Fusion

Image fusion integrates complementary information from multiple sources, yet most recent systems still learn to reconstruct every output pixel with patch training, long schedules, or large pretrained backbones. We propose Hybrid Fusion, a source-allocation formulation in which a U-Net predicts only a dense guidance map and a fixed Laplacian-pyramid kernel performs synthesis. The output therefore remains an explicit multi-scale mixture of the two sources, while the learnable task is reduced from image generation to deciding where each source should contribute. This design supports full-resolution optimization, reaches strong infrared-visible fusion quality after one to two epochs, improves downstream detection, and transfers without retraining to medical and video fusion. A unified single-GPU benchmark further shows that the practical advantage comes from rapid adaptation rather than a selectively cheap iteration. For accessibility, our Colab demo reproduces the one-epoch result in about five minutes on a free Google T4 GPU.