Infrared and visible image fusion is an active area of research as it provides fused image with better scene information and sharp features. An efficient fusion of images from multisensory sources is always a challenge for researchers. In this paper, an efficient image fusion method based on sparse representation with clustered dictionary is proposed for infrared and visible images. Firstly, the edge information of visible image is enhanced by using a guided filter. To extract more edge information from the source images, modified spatial frequency is used to generate a clustered dictionary from the source images. Then, non-subsampled contourlet transform (NSCT) is used to obtain low-frequency and high-frequency sub-bands of the source images. The low-frequency sub-bands are fused using sparse coding, and the high-frequency sub-bands are fused using max-absolute rule. The final fused image is obtained by using inverse NSCT. The subjective and objective evaluations show that the proposed method is able to outperform other conventional image fusion methods.

红外和可见光图像融合是一个活跃的研究领域，因为它为融合图像提供了更好的场景信息和鲜明的特征。来自多感官来源的图像的有效融合始终是研究人员面临的挑战。针对红外和可见光图像，提出了一种基于稀疏表示和聚类字典的有效图像融合方法。首先，通过使用引导滤波器来增强可见图像的边缘信息。为了从源图像中提取更多的边缘信息，使用修改的空间频率从源图像中生成聚类字典。然后，使用非下采样轮廓波变换（NSCT）来获取源图像的​​低频和高频子带。低频子带使用稀疏编码融合，然后使用最大绝对规则对高频子带进行融合。通过使用反NSCT获得最终的融合图像。主观和客观评估表明，所提出的方法能够胜过其他传统的图像融合方法。