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5 November 2014 Image fusion driven by the analysis of sparse coefficients
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This paper proposes an efficient fusion method for multiple remote sensing images based on sparse representation, in which we mainly solve the fusion rules of the sparse coefficients. In the proposed fusion method, first is to obtain the sparse coefficients of different source images based on three dictionaries. Considering the sparsity, the source coefficients can be divided into large, middle, and small correlation classer. According to the analysis and comparison of permutations, the final coefficients are fused in the term of different fusion rules according to the correlation. Finally, the fused image can be reconstructed via combining the fused coefficients and trained dictionaries.
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Xiujuan Yu, Hanwen Zhao, Xiaoyan Luo, and Ding Yuan "Image fusion driven by the analysis of sparse coefficients", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92731Z (5 November 2014);


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