Multimodal image fusion via coupled feature learning.

• A general learning-based decomposition model suitable for fusing images from various imaging modalities is proposed. • The multimodal images are decomposed into correlated and uncorrelated components. • A CDL method based on simultaneous sparse approximation is proposed for estimating the correlated features. • The uncorrelated components are estimated using a Pearson correlation-based constraint. This paper presents a multimodal image fusion method using a novel decomposition model based on coupled dictionary learning. The proposed method is general and can be used for a variety of imaging modalities. In particular, the images to be fused are decomposed into correlated and uncorrelated components using sparse representations with identical supports and a Pearson correlation constraint, respectively. The resulting optimization problem is solved by an alternating minimization algorithm. Contrary to other learning-based fusion methods, the proposed approach does not require any training data, and the correlated features are extracted online from the data itself. By preserving the uncorrelated components in the fused images, the proposed fusion method significantly improves on current fusion approaches in terms of maintaining the texture details and modality-specific information. The maximum-absolute-value rule is used for the fusion of correlated components only. This leads to an enhanced contrast-resolution without causing intensity attenuation or loss of important information. Experimental results show that the proposed method achieves superior performance in terms of both visual and objective evaluations compared to state-of-the-art image fusion methods. [ABSTRACT FROM AUTHOR]