Title: Rethinking Few-Shot Image Fusion: Granular Ball Priors Enable General-Purpose Deep Fusion Show Chinese title

Title: 重思Few-Shot图像融合：粒球先验实现通用深度融合

Abstract: In image fusion tasks, the absence of real fused images as priors presents a fundamental challenge. Most deep learning-based fusion methods rely on large-scale paired datasets to extract global weighting features from raw images, thereby generating fused outputs that approximate real fused images. In contrast to previous studies, this paper explores few-shot training of neural networks under the condition of having prior knowledge. We propose a novel fusion framework named GBFF, and a Granular Ball Significant Extraction algorithm specifically designed for the few-shot prior setting. All pixel pairs involved in the fusion process are initially modeled as a Coarse-Grained Granular Ball. At the local level, Fine-Grained Granular Balls are used to slide through the brightness space to extract Non-Salient Pixel Pairs, and perform splitting operations to obtain Salient Pixel Pairs. Pixel-wise weights are then computed to generate a pseudo-supervised image. At the global level, pixel pairs with significant contributions to the fusion process are categorized into the Positive Region, while those whose contributions cannot be accurately determined are assigned to the Boundary Region. The Granular Ball performs modality-aware adaptation based on the proportion of the positive region, thereby adjusting the neural network's loss function and enabling it to complement the information of the boundary region. Extensive experiments demonstrate the effectiveness of both the proposed algorithm and the underlying theory. Compared with state-of-the-art (SOTA) methods, our approach shows strong competitiveness in terms of both fusion time and image expressiveness. Our code is publicly available at: Show Chinese abstract

Abstract: 在图像融合任务中，缺乏真实的融合图像作为先验知识提出了一个根本性的挑战。大多数基于深度学习的融合方法依赖于大规模配对数据集，从原始图像中提取全局加权特征，从而生成近似真实融合图像的融合输出。与以往的研究不同，本文探讨了在有先验知识条件下神经网络的少量样本训练。我们提出了一种名为GBFF的新融合框架，以及一种专门设计用于少量样本先验设置的粒球显著性提取算法。所有参与融合过程的像素对最初被建模为粗粒度粒球。在局部层面，细粒度粒球用于滑动遍历亮度空间以提取非显著像素对，并执行分割操作以获得显著像素对。然后计算像素级权重以生成伪监督图像。在全局层面，对融合过程有显著贡献的像素对被分类到正区域，而那些贡献无法准确确定的则被分配到边界区域。粒球根据正区域的比例进行模态感知适应，从而调整神经网络的损失函数，使其能够补充边界区域的信息。广泛的实验验证了所提出算法和基础理论的有效性。与最先进的（SOTA）方法相比，我们的方法在融合时间和图像表现力方面表现出很强的竞争力。我们的代码公开可获取于：