Remote image fusion using window empirical mode decomposition

By introduction of an adaptive window function whose nature and scale vary with local data characteristics, WEMD can remove the gray spots in the component image of the original two-dimensional EMD. Therefore, it is more appropriate for the analysis of the image details. When WEMD is introduced to remote image fusion, the characteristics of original images are taken better and more information for fusion is obtained. Firstly, the source images are decomposed using WEMD with its ability in the acquirement of the high frequency data gotten and its adaptive features into some intrinsic mode functions(IMFs) and the residual component, then the IMFs and the residual component are fused with the detail/background and energy contribution fusion regular respectively in the corresponding scales. And finally the fused IMFs and the residual component are reconstructed to obtain fusion results. The subjective and objective results of the experiment have shown that the proposed algorithm is efficient in image fusion and better than other algorithms.