Infrared and visible images fusion using visual saliency and optimized spiking cortical model in non-subsampled shearlet transform domain

Abstract

Aiming at some problems in existing infrared and visible image fusion methods such as edge blurring, low contrast, loss of details, a novel fusion scheme based on non-subsampled shearlet transform (NSST), visual saliency and multi-objective artificial bee colony (MOABC) optimizing spiking cortical mode (SCM) is proposed. NSST has many advantages such as multi-scale features and sparse representation. Moreover, the visual saliency map can improve the low frequency fusion strategy, and SCM has coupling and pulse synchronization properties. Firstly, NSST is utilized to decompose the source image into a low-frequency subband and a series of high-frequency subbands. Secondly, the low-frequency subband is fused by SCM, where SCM is motivated by the edge saliency map of the low-frequency subband of the source image, and then the high-frequency subbands are also fused by SCM, where the modified spatial frequency of the high-frequency subbands of the source image is adopted as the input stimulus of SCM, the parameters of SCM are optimized by the novel multi-objective artificial bee colony technique. Finally, the fused image is reconstructed by inverse NSST. Experimental results indicate that the proposed scheme performs well and has obvious superiorities over other current typical ones in both subjective visual performance and objective criteria.