Multimodal image fusion aims to generate a fused image from different signals that captured by multimodal sensors. Although the images obtained by multimodal sensors have different appearances, the information included in these images might be redundant and noisy. In the previous studies, the fusion rule and their properties that guiding how to merge the features from multiple images is relatively simple functions such as choose-max or weighted average. However, merging the features with redundant information based on these fusion rules may lead to brightness distortion or extra noises, since these fusion rules ignore the spatial consistency of feature selections. In this paper, we propose a novel multimodal image fusion algorithm that focuses on both the transferring the salient structures and maintaining spatial consistency that we consider as fundamental to build our proposed architecture. The proposed algorithm selects features to be transferred into fusion results by a graph cut algorithm, in which the spatial varying smoothness cost is formulated based on the independence between local features measured by point-wise mutual information (PMI). Experiment results demonstrate that, with the straightforward gradient features, the proposed method can obtain state-of-the-art performance on several publicly available multimodal image databases.

多峰图像融合旨在根据多峰传感器捕获的不同信号生成融合图像。尽管通过多模式传感器获得的图像具有不同的外观，但是这些图像中包含的信息可能是多余且嘈杂的。在先前的研究中，指导如何合并多个图像中的特征的融合规则及其属性是相对简单的功能，例如select-max或加权平均值。但是，基于这些融合规则将特征与冗余信息合并可能会导致亮度失真或额外的噪声，因为这些融合规则忽略了特征选择的空间一致性。在本文中，我们提出了一种新颖的多峰图像融合算法，该算法专注于显着结构的转移和保持空间一致性，我们认为这是构建拟议架构的基础。提出的算法通过图割算法选择要转移到融合结果中的特征，其中，空间局部平滑度的代价是基于局部特征之间的独立性来确定的。点向互信息（PMI）。实验结果表明，利用简单的梯度特征，该方法可以在几个公开的多峰图像数据库上获得最新的性能。