Abstract: Considering the problems of low contrast,blocky effects,artifacts and distortion of the edge region in traditional fused images,this paper proposed an infrared image and visible image fusion method based on fourth order partial differential equations(FPDEs) and cross bilateral filter(CBF).Firstly,the FPDEs and CBF are respectively used to obtain the approximation layers and detail layers from the source image.Secondly,the approximate layers obtained by multi-scale decompositions may contain amount of residual low-frequence information which will result in large contrast of the overall visual of the fused image,so a fusion method based on visual saliency map(VSM) is used to fuse the approximate layers.Thirdly,an improved Karhunen-Loeve transform is applied into the detail layer to obtain the optimal weights for fusion.Finally,a fused image is generated from the linear combination of final approximate layers and detail layers.Experimental results show that the standard deviation of the fused image obtained by the proposed method increases about 43.3% than PCA based method and cross bilateral filter based method,and the average gradient and spatial frequency increase about 9.46% and 19.79% respectively on average compared with GFF and VSM_WLS algorithms.

Key words: Cross bilateral filter, Fourth order partial differential equations, Image fusion, Karhunen-Loeve transform, Visual saliency map