In this paper

we propose a concept of corner measurement according to Harris corner detection principle

and take corner measure amplitude as a choice of fused coefficients for high-frequency image. And then the significant central coefficient (SCC) image fusion algorithm based on corner measure is presented in redundant wavelet field. The basic idea of the proposed algorithm is first to decompose an image into wavelet planes and approximate planes by using redundant wavelet transform

and then use the corner measurement response function to evaluate corner measure amplitude for the wavelet plane. Significant central coefficient (SCC) image fusion algorithm based on corner measure fusion rule is adopted for wavelet planes. For the approximate planes

average coefficient fusion rule is adopted. Finally

the fusion image was obtained by taking redundant wavelet inverse transform. In the experiment

we adopt multi-spectral data of Clementine lunar surface and multi-spectral data of SPOT5 to verify the validity of our algorithm

and we also compare it with other methods. Beside the subjective comparison based on vision

we introduce objective evaluation index such as the standard deviation

entropy

clarity and the correlation coefficient of the integration to evaluate fusion results. The experiments show that the algorithm can maintain details information feature such as the corner and edge of source images more effectively.