Existing image fusion methods primarily focus on solving single-task fusion problems, overlooking the potential information complementarity among multiple fusion tasks. Additionally, there has been no prior research in the field of image fusion that explores the mixed training of labeled and unlabeled data for different fusion tasks. To address these gaps, this paper introduces a novel multi-task semi-supervised learning approach to construct a general image fusion framework. This framework not only facilitates collaborative training for multiple fusion tasks, thereby achieving effective information complementarity among datasets from different fusion tasks, but also promotes the (unsupervised) learning of unlabeled data via the (supervised) learning of labeled data. Regarding the specific network module, we propose a so-called pseudo-siamese Laplacian pyramid transformer (PSLPT), which can effectively distinguish information at different frequencies in source images and discriminatively fuse features from distinct frequencies. More specifically, we take datasets of four typical image fusion tasks into the same PSLPT for weight updates, yielding the final general fusion model. Extensive experiments demonstrate that the obtained general fusion model exhibits promising outcomes for all four image fusion tasks, both visually and quantitatively. Moreover, comprehensive ablation and discussion experiments corroborate the effectiveness of the proposed method. The code is available at .

中文翻译：

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使用多任务半监督学习的通用图像融合框架

现有的图像融合方法主要侧重于解决单任务融合问题，忽视了多个融合任务之间潜在的信息互补性。此外，图像融合领域之前还没有研究探索针对不同融合任务的标记和未标记数据的混合训练。为了解决这些差距，本文引入了一种新颖的多任务半监督学习方法来构建通用图像融合框架。该框架不仅促进了多个融合任务的协作训练，从而实现不同融合任务的数据集之间的有效信息互补，而且还通过标记数据的（监督）学习促进了无标记数据的（无监督）学习。关于具体的网络模块，我们提出了一种所谓的伪暹罗拉普拉斯金字塔变换器（PSLPT），它可以有效地区分源图像中不同频率的信息，并有区别地融合不同频率的特征。更具体地说，我们将四个典型图像融合任务的数据集放入同一个 PSLPT 中进行权重更新，产生最终的通用融合模型。大量的实验表明，所获得的通用融合模型在视觉和定量方面对所有四种图像融合任务都表现出了有希望的结果。此外，全面的消融和讨论实验证实了该方法的有效性。该代码可在 处获取。