Regional Feature Self-Adaptive Image Fusion Method Based on Coordinated Bidimensional Empirical Mode Decomposition

To conquer the weakness of existing in traditional image fusion method based on bidimensional empirical mode decomposition(BEMD), a novel fusion algorithm of multi-sensor images based on coordinated bidimensional empirical mode decomposition(C-BEMD) is proposed in this paper. Firstly, the source images are decomposed by C-BEMD to obtain the intrinsic mode function(IMF) components and residue components. Then, for the IMF components, a selection and weighted average fusion rule based on the local area energy is adopted. For the residue components, a selection and weighted average strategy based on local neighborhood visibility is presented. Finally, the fused image is obtained by performing the inverse C-BEMD on the combined coefficients. Experimental results show that the proposed approach provides superior performance over the image fusion methods based on wavelet transform, line and column crossed-used BEMD and complex empirical mode decom-position in terms of both visual quality and objective evaluation criteria.