Infrared and visible image fusion based on NSST and phase consistency adaptive DUAL channel PCNN

Abstract

The infrared and visible image fusion has been one of the significant research hotspots in image processing field. This paper presents a new IR and VI image fusion framework, which constructs a parameter adaptive dual channel pulse coupled neural network (PADCPCNN) with phase consistency in the non-subsampled shearlet transform (NSST) domain. Firstly, the original images are decomposed into low-pass and high-pass subbands by adopting NSST. Secondly, the weighted average fusion strategy based on phase consistency is applied to merge the low-pass subbands, and high-pass subbands are fused by the PADCPCNN with phase consistency. Additionally, in order to solve the information difference and edge detail problem of two source images, the phase consistency operator is exploited as the adaptive connection strength. Furthermore, four sets of controlled experiments are designed and fusion comparative experiments are conducted with images of typical datasets, from different backgrounds to make objective and subjective comparison. And their evaluation results show that the proposed algorithm highlights more texture and edge details than other existing main fusion methods.