In image fusion tasks, the absence of real fused images as supervision signals poses significant challenges for supervised learning. Existing deep learning methods typically address this issue either by designing handcrafted priors or by relying on large-scale datasets to learn model parameters. Different from previous approaches, this paper introduces the concept of incomplete priors, which formally describe handcrafted priors at the algorithmic level and estimate their confidence. Based on this idea, we couple incomplete priors with the neural network through a sample-level adaptive loss function, enabling the network to learn and re-infer fusion rules under conditions that approximate the real fusion process.To generate incomplete priors, we propose a Granular Ball Pixel Computation (GBPC) algorithm based on the principles of granular computing. The algorithm models fused-image pixels as information units, estimating pixel weights at a fine-grained level while statistically evaluating prior reliability at a coarse-grained level. This design enables the algorithm to perceive cross-modal discrepancies and perform adaptive inference.Experimental results demonstrate that even under few-shot conditions, a lightweight neural network can still learn effective fusion rules by training only on image patches extracted from ten image pairs. Extensive experiments across multiple fusion tasks and datasets further show that the proposed method achieves superior performance in both visual quality and model compactness. The code is available at: https://github.com/DMinjie/GBFF

翻译：在图像融合任务中，缺乏真实融合图像作为监督信号给监督学习带来了重大挑战。现有的深度学习方法通常通过设计手工先验或依赖大规模数据集学习模型参数来解决这一问题。不同于以往方法，本文引入了不完全先验的概念，从算法层面形式化描述手工先验并评估其置信度。基于这一思想，我们通过样本级自适应损失函数将不完全先验与神经网络耦合，使网络能在近似真实融合过程的条件下学习并重新推断融合规则。为生成不完全先验，我们基于粒度计算原理提出了粒度球像素计算（GBPC）算法。该算法将融合图像像素建模为信息单元，在细粒度层面估计像素权重，同时在粗粒度层面统计评估先验可靠性。这一设计使算法能够感知跨模态差异并执行自适应推理。实验结果表明，即使在少样本条件下，仅通过从十对图像中提取的图像块进行训练，轻量级神经网络仍能学习有效的融合规则。在多个融合任务和数据集上的广泛实验进一步表明，所提方法在视觉质量和模型紧凑性方面均取得了优越性能。代码发布于：https://github.com/DMinjie/GBFF