A level-set approach to image blending

This paper presents a novel method for blending images, Image blending refe rs to the process of creating a set of discrete samples of a continuous, on e-parameter family of images that connects a pair of input images. Image bl ending has uses in a variety of computer graphics and image processing appl ications. In particular, it can be used for image morphing, which is a meth od for creating video streams that depict transformations of objects in sce nes based solely on pairs of images and sets of user-defined fiducial point s. Image blending also has applications for video compression and image-bas ed rendering. The proposed method for image blending relies on the progressive minimizati on of a difference metric which compares the level sets between two images. This strategy results in an image blend which is the solution of a pair of coupled, nonlinear, first-order, partial differential equations that model multidimensional level-set propagations. When compared to interpolation th is method produces more natural appearances of motion because it manipulate s the shapes of image contours rather than simply interpolating intensity v alues. This strategy results in a process that has the qualitative property of deforming greyscale objects in images rather than producing a simple fa de from one object to another. This paper presents the mathematics that und erlie this new method, a numerical implementation, and results on real imag es that demonstrate its effectiveness.