An improved hybrid multiscale fusion algorithm based on NSST for infrared-visible images

The key to improving the fusion quality of infrared-visible images is effectively extracting and fusing complementary information such as bright-dark information and saliency details. For this purpose, an improved hybrid multiscale fusion algorithm inspired by non-subsampled shearlet transform (NSST) is proposed. In this algorithm, firstly, the support value transform (SVT) is used instead of the non-subsampled pyramid as the frequency separator to decompose an image into a set of high-frequency support value images and one low-frequency approximate background. These support value images mainly contain the saliency details from the source image. And then, the shearlet transform of NSST is retained to further extract the saliency edges from these support value images. Secondly, to extract the bright-dark details from the low-frequency approximate background, a morphological multiscale top-bottom hat decomposition is constructed. Finally, the extracted information is combined by different rules and the fused image is reconstructed by the corresponding inverse transforms. Experimental results have shown the proposed algorithm has obvious advantages in retaining saliency details and improving image contrast over those state-of-the-art algorithms.

Automated summary

An improved hybrid multiscale fusion algorithm inspired by NSST is proposed to effectively extract and fuse complementary information in infrared-visible images, resulting in improved image contrast and retained saliency details.