DMMIF

Multi-modality image fusion aims to combine diverse images into a single image that captures important targets, intricate texture details, and enables advanced visual tasks. Existing fusion methods often overlook the complementarity of difffferent modalities by treating source images uniformly and extracting similar features. This study introduces a distributed optimization model that leverages a collection of images and their signifificant features stored in distributed storage. To solve this model, we employ the distributed Alternating Direction Method of Multipliers (ADMM) algorithm. Based on this framework, we propose the DMMIF, a distributed multi-modality image fusion algorithm 专为红外和可见光图像融合、医学图像融合以及遥感图像而设计融合。DMMIF effff有效地整合了来自源图像的补充信息，同时最大限度地减少了 融合的图像。实验结果表明，与状态相比，我们的算法具有优越的性能使用公开可用数据集的现有方法。我们的方法准确地表示源图像，并且意义重大提高融合质量，通过视觉和定量评估得到证实。