Infrared and Visible-Light Image Fusion Based on FCM and Guided Filtering

To solve the problems of vague targets, detail loss, and algorithm instability in traditional infrared and visible-light image fusion algorithms, a fusion method based on fuzzy c-means (FCM) clustering and guided filtering is proposed. The low-frequency sub-band was enhanced by guided filtering after applying a non-subsampled shearlet transform (NSST) to the original image. The low- and high-frequency sub-bands were then fused using FCM clustering and a dual-channel spiking cortical model. Finally, the fused image was obtained using an inverse NSST transform. The experimental results showed that the proposed algorithm was stable, the fusion image had clear targets and relatively complete details in the subjective evaluation, and the algorithm had an excellent standard deviation, mutual information, average gradient, information entropy, and edge retention factor in the objective evaluation.