摘要

The feature information extracted by using activity measure, a very important measure in the process of image fusion, determine a certain input image with more obvious characteristics. In wavelet-based fusion algorithm, the common activity measures consider the coefficients of all high-frequency sub-bands themselves only, and ignore the information provided by low-frequency coefficients. An algorithm based on the general method and added the entropy-masking obtained from the low-frequency coefficients as an active measure is proposed in this paper to comprehensively consider the impact of the high and low frequency coefficients on the activity measures. And, the fused image with different activity measures and objective performance evaluation index are provided also. The results show that the entropy-masking measure is better than several other traditional measures for image fusion.

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