Abstract:

A new method for image fusion based on Contourlet
transform and cycle spinning is proposed. Contourlet transform is
a flexible multiresolution, local and directional image expansion,
also provids a sparse representation for two-dimensional piecewise
smooth signals resembling images. Due to lack of translation
invariance property in Contourlet transform, the conventional image
fusion algorithm based on Contourlet transform introduces
many artifacts. According to the theory of cycle spinning applied to
image denoising, an invariance transform can reduce the artifacts
through a series of processing efficiently. So the technology of
cycle spinning is introduced to develop the translation invariant
Contourlet fusion algorithm. This method can effectively eliminate
the Gibbs-like phenomenon, extract the characteristics of original
images, and preserve more important information. Experimental
results show the simplicity and effectiveness of the method and its
advantages over the conventional approaches.