Application of mathematical morphological levelings scale-space to image fusion

In the multi-scale image transforms, scale-spaces which are built on linear operators such as gaussian filter, have the same important drawback: edges in the image are blurred and the contours of objects are distorted. Mathematical morphological levelings(MML), as powerful operators that possess a number of desired properties for the construction of nonlinear scale-space, can overcome this drawback. Considering the advantages of MML, this paper applies the MML scale-space in image fusion, in order to improve the visual effect of fused image. Firstly, we analyze different methods that compose the MML operators, and different methods of generating marker image, choose proper method from them. Secondly, suitable fusion rules are carefully chosen for different kinds of image fusion. Finally, observing that the MML can generate rather fine detail images, enhancement factors are added to improve the effect of fused image. Experimental results show that our method can better preserve the nonlinear features in the fused image, such as sharp edges in multifocus image fusion, and high intensity objects in visible/infrared image fusion. Thus, it performs better than popular linear scale-space based image fusion methods, such as -trous wavelets and Nonsubsampled Contourlet Transform(NSCT).