摘要

We combine the multi-scale analysis in wavelet domain with the single-scale sparse representation in image domain and propose an image fusion algorithm based on multi-scale dictionary learning. We transform the trained images into wavelet domain and train the dictionary for each sub-band dictionary. We use the trained dictionary to solve and fuse the sparse representation coefficient of each sub-band of a source image. The fused image is reconstructed through the inverse wavelet domain. Our algorithm combines the sparse character of a learned dictionary with the multi-resolution character of wavelet analysis. The experimental results, given in Fig. 2 and Table 1, and their analysis show that our image fusion algorithm outperforms those based on the learned dictionary in image domain and multi-scale analysis in wavelet domain respectively.

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