Infrared and visible image fusion via NSCT and gradient domain PCNN

Abstract

Infrared and visible image fusion can obtain an integrated image containing obvious object information and high spatial resolution background information. Therefore, combining the characteristics of infrared and visible images to obtain the fused image has important research significance. In this paper, an effective fusion algorithm based on non-subsampled contourlet transform (NSCT) is proposed. The method is based on the application of a modulated pulse-coupled neural network fusion (PCNN) strategy and an energy attribute fusion strategy in the NSCT domain. First, NSCT is used to decompose the input original image into low frequency sub-images and high frequency sub-images. Then, the high frequency sub-images are fused via a multi-level morphological gradient (MLMG) domain PCNN and the low frequency sub-images are fused via the energy attribute fusion strategy. Finally, the fused sub-images are reconstructed by inverse NSCT. Experimental results demonstrate that the proposed algorithm has a better fusion performance in both subjective evaluation and objective evaluation.