根据Harris角点检测原理，提出角点测度的概念，并以角点测度响应值作为高频图像融合系数的选择依据，进而提出基于图像冗余小波域的角点测度重要中心系数算法。算法首先利用冗余小波变换把多光谱图像分解成小波平面和相似平面，然后利用角点测度响应函数来估计小波平面的角点测度，用基于角点测度响应值的重要中心系数融合规则融合小波平面。对相似平面则采取加权平均的融合规则，最后通过冗余小波逆变换得到融合图像。在实验中，用Clementine月球表面多光谱数据和SPOT5多光谱数据验证了算法的有效性，并和其他方法做了比较，除了基于视觉的主观比较以外，还引入了标准差、熵、清晰度和相关系数等客观评价指标对融合结果进行评价，结果表明，算法有效地保持了原图像的细节特征，如边缘、角点等。

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.