Rethinking Few-Shot Fusion: Granular Ball Priors Enable General-Purpose Deep Image Fusion

In image fusion tasks, due to the lack of real fused images as priors, most deep learning-based fusion methods obtain global weight features from original images in large-scale data pairs to generate images that approximate real fused images. However, unlike previous studies, this paper utilizes Granular Ball adaptation to extract features in the brightness space as priors for deep networks, enabling the fusion network to converge quickly and complete the fusion task. This leads to few-shot training for a general image fusion network, and based on this, we propose the GBFF fusion method. According to the information expression division of pixel pairs in the original fused image, we classify pixel pairs with significant performance as the positive domain and non-significant pixel pairs as the boundary domain. We perform split inference in the brightness space using Granular Ball adaptation to compute weights for pixels that express information to varying degrees, generating approximate supervision images that provide priors for the neural network in the structural brightness space. Additionally, the extracted global saliency features also adaptively provide priors for setting the loss function weights of each image in the network, guiding the network to converge quickly at both global and pixel levels alongside the supervised images, thereby enhancing the expressiveness of the fused images. Each modality only used 10 pairs of images as the training set, completing the fusion task with a limited number of iterations. Experiments validate the effectiveness of the algorithm and theory, and qualitative and quantitative comparisons with SOTA methods show that this approach is highly competitive in terms of fusion time and image expressiveness.