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.

中文翻译：

---

在NSCT域中基于显着性的多模态图像融合

图像融合旨在聚集多个原始图像中的冗余和互补信息，最具挑战性的方面是设计鲁棒的功能和判别模型，以增强融合图像中的显着性信息。为了解决这个问题，作者开发了一种新颖的图像融合算法来保留多模式图像的不变知识。具体来说，他们基于非下采样轮廓波变换（NSCT）制定了一种新颖的统一架构。他们的方法引入了四叉树分解和贝塞尔插值，以提取关键的红外特征。此外，他们分别针对低通和高通子带融合提出了基于显着性广告阶段一致性的规则和基于局部拉普拉斯能量的规则。用这种方法 融合图像不仅可以融合源图像的局部和全局特征，避免平滑目标边缘，而且可以保留较小的比例尺细节并抵御多模态图像的干扰噪声。实验结果的客观评估和主观视觉都表明，该算法在客观评估标准和视觉质量上均具有竞争力。