Infrared and visible image fusion based on nonsubsampled shearlet transform and fuzzy C-means clustering

To preserve more useful information in visible and infrared images and improve the quality of the fused image, a method based on the nonsubsampled shearlet transform (NSST) and fuzzy C-means clustering is proposed. First, the source images are decomposed by NSST so as to get their own low- and high-frequency subbands. Second, the low-frequency subbands are divided into the infrared target part and the background part by fuzzy C-means clustering while different fusion rules are applied to the infrared target part and background part, respectively. Then, a choose-max fusion rule based on the sum-modified Laplacian of source images and local energy of coefficient is proposed to integrate the high-frequency subbands. Finally, the fused image is obtained by inverse NSST. The comparison experiment with the other three state-of-the-art fusion methods shows that the proposed method has good subjective visual effects and superior objective evaluations. (C) 2018 SPIE and IS&T