Multimodal image fusion is the process of combing relevant biological information that can be used for automated industrial application. In this article, we present a novel framework combining fractal constraint with group sparsity to achieve the optimal fusion quality. First, we adopt the idea of patch division and componentwise separation to perceive the fractal characteristics across multimodality sources. Then, to preserve the spatial information against the redundancy of component-entanglement, the group sparsity is proposed. A dual variable weighting rule is inherently embedded to mitigate the overfitting across the component penalty. Furthermore, the alternating direction method of multipliers is conducted to the proposed model optimization. The experiments show that our model has a better performance in quantitative visual quality and qualitative evaluation analysis. Finally, a real segmentation application of positron emission tomography/computed tomography image fusion proves the effectiveness of our algorithm.

中文翻译：

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FCFusion：用于医学图像融合的具有组稀疏性的分形分量建模

多模态图像融合是结合相关生物信息的过程，可用于自动化工业应用。在本文中，我们提出了一种将分形约束与组稀疏性相结合的新框架，以实现最佳融合质量。首先，我们采用补丁划分和组件分离的思想来感知跨多模态源的分形特征。然后，为了保留空间信息以防止组件纠缠的冗余，提出了组稀疏性。固有地嵌入了对偶变量加权规则，以减轻组件损失的过度拟合。此外，对所提出的模型优化进行了乘法器的交替方向方法。实验表明，我们的模型在定量视觉质量和定性评价分析方面有较好的表现。最后，正电子发射断层扫描/计算机断层扫描图像融合的真实分割应用证明了我们算法的有效性。