Image fusion method based on improved NSCT transform and PCNN model

Multi-source image fusion has been the hotspot of research in recent years, in order to make the fusion results more consistent with human vision and computer processing, many classical algorithms have been proposed, such as contourlet transform, non-subsampled contourlet transform (NSCT) and so on. On this basis, this paper proposes an improved fusion method of infrared and visible images based on NSCT transform and pulse coupled neural network (PCNN). The basic idea is that the source images are decomposed by NSCT to obtain low frequency and high frequency bands, low frequency components use the regional variance saliency fusion method of adaptive threshold weighted, high frequency components use the PCNN method, and high frequency subband coefficients are directly used as external inputs of PCNN neurons. Experimental results show that the fused image has higher clarity and richer useful information. Both subjective and objective performance indicators are better than the traditional algorithm.