Infrared and visible image fusion based on FUDPCNN and gravitational force operator

Infrared and visible image fusion merges the salient details of an infrared and its respective visible image to create a formidable image more suitable for surveillance, image enhancement, object detection, and remote sensing. This paper presents a multi-scale transform-based infrared and visible image fusion method in the non-subsampled contourlet transform domain. The proposed method utilizes a new fast unit-linking dual-channel pulse coupled neural network (FUDPCNN) model. The low-pass sub-bands are fused by a new gravitational force operator-based mechanism. On the other hand, the internal activities of the proposed FUDPCNN are applied to get the fused high-pass sub-bands. The effectiveness of the FUDPCNN is shown by comparing it with practiced PCNN models. Moreover, the competitiveness of the gravitational force operator-based rule is described using other low-pass rules. The workability of the proposed method is shown by comparing its fusion outcomes on well-known infrared-visible image pairs with the nine existing approaches using eight objective metrics.