Multi-modal image fusion based on saliency guided in NSCT domain

Image fusion aims at aggregating the redundant and complementary information in multiple original images, the most challenging aspect is to design robust features and discriminant model, which enhances saliency information in the fused image. To address this issue, the authors develop a novel image fusion algorithm for preserving the invariant knowledge of the multimodal image. Specifically, they formulate a novel unified architecture based on non-subsampled contourlet transform (NSCT). Their method introduces Quadtree decomposition and Bezier interpolation to extract crucial infrared features. Furthermore, they propose a saliency advertising phase congruency-based rule and local Laplacian energy-based rule for low- and high-pass sub-bands fusion, respectively. In this approach, the fusion image could not only combine the local and global features of the source image to avoid smoothing the edge of the target, but also retain the minor scales details and resists the interference noise of the multi-modal image. Both objective assessments and subjective visions of experimental results indicate that the proposed algorithm performs competitively in both objective evaluation criteria and visual quality.