As a common image processing technique, image decomposition is often used to extract complementary information between modalities. In current decomposition-based image fusion methods, typically, source images are decomposed into three parts at single scale (i.e., visible-exclusive part, infrared-exclusive part, and common part) and lacking interaction between modalities during the decomposition process. These results in the inability of fusion images to effectively focus on finer complementary information between modalities at various scales. To address the above issue, a novel decomposition mechanism, Continuous Decomposition Fusion (CDeFuse), is proposed. Firstly, CDeFuse extends the original three-part decomposition to a more general K-part decomposition at each scale through similarity constraints to fuse multi-scale information and achieve a finer representation of decomposition features. Secondly, a Continuous Decomposition Module (CDM) is introduced to assist K-part decomposition. Its core component, State Transformer (ST), efficiently captures complementary information between modalities by utilizing multi-head self-attention mechanism. Finally, a novel decomposition loss function and the corresponding computational optimization strategy are utilized to ensure the smooth progress of the decomposition process while maintaining linear growth in time complexity with the number of decomposition results K. Extensive experiments demonstrate that our CDeFuse achieves comparable performance compared to previous methods. The code will be publicly available.

翻译：作为一种常见的图像处理技术，图像分解常被用于提取模态间的互补信息。在现有的基于分解的图像融合方法中，通常将源图像在单一尺度下分解为三部分（即可见光独占部分、红外独占部分及共有部分），且分解过程中缺乏模态间的交互。这导致融合图像无法有效聚焦于不同尺度下模态间更精细的互补信息。针对上述问题，本文提出一种新颖的分解机制——连续分解融合（CDeFuse）。首先，CDeFuse通过相似性约束将原始的三部分分解扩展为各尺度下更通用的K部分分解，以融合多尺度信息并实现分解特征的更精细表示。其次，引入连续分解模块（CDM）辅助K部分分解，其核心组件状态转换器（ST）利用多头自注意力机制高效捕获模态间的互补信息。最后，采用一种新颖的分解损失函数及相应的计算优化策略，在确保分解过程平稳进行的同时，使时间复杂度随分解结果数量K保持线性增长。大量实验表明，我们的CDeFuse相较于现有方法取得了具有竞争力的性能。代码将公开提供。