Abstract: Aiming at uneven brightness,inconspicuous object,low contrast and loss details problems in the existing infrared and visible light image fusion methods,in combination with nonsubsampled shearlet transform (NSST) which has multi-scale transformation and the most sparse expression characteristics,saliency detection which has the advantage of highlighting infrared objects,and Dual-channel pulse coupled neural network(Dual-PCNN)which has the advantages of coupling and pulse synchronization,an image fusion method for infrared and visible light images based on NSST and visual saliency guide Dual-PCNN was proposed in this paper.Firstly,the high frequency and low frequency sub-band coefficients of infrared and visible light image are decomposed by NSST in each direction,and then low frequency coefficients are fused by the Dual-PCNN,which is guided by the saliency map of the images.For the high frequency sub-band coefficients,a modified spatial frequency is adopted as the input to motivate the Dual-PCNN.Finally,the fused image is reconstructed by inverse NSST.The experimental results demonstrate that the infrared objects in the fusion image are highlighted and the details of the visible background are rich.Compared with other fusion algorithms,the proposed method has a certain degree of improvement on the subjective evaluation and objective evaluation.

Key words: Dual-channel pulse coupled neural network, Image fusion, Nonsubsampled shearlet transform, Visual saliency