Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

Xinqiang Qin; Gang Hu; Kai Hu

doi:10.1117/1.JEI.27.1.013017

12 February 2018 Image fusion method based on regional feature and improved bidimensional empirical mode decomposition

Xinqiang Qin, Gang Hu, Kai Hu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 1, 013017 (February 2018). https://doi.org/10.1117/1.JEI.27.1.013017

Abstract

The decomposition of multiple source images using bidimensional empirical mode decomposition (BEMD) often produces mismatched bidimensional intrinsic mode functions, either by their number or their frequency, making image fusion difficult. A solution to this problem is proposed using a fixed number of iterations and a union operation in the sifting process. By combining the local regional features of the images, an image fusion method has been developed. First, the source images are decomposed using the proposed BEMD to produce the first intrinsic mode function (IMF) and residue component. Second, for the IMF component, a selection and weighted average strategy based on local area energy is used to obtain a high-frequency fusion component. Third, for the residue component, a selection and weighted average strategy based on local average gray difference is used to obtain a low-frequency fusion component. Finally, the fused image is obtained by applying the inverse BEMD transform. Experimental results show that the proposed algorithm provides superior performance over methods based on wavelet transform, line and column-based EMD, and complex empirical mode decomposition, both in terms of visual quality and objective evaluation criteria.

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Xinqiang Qin, Gang Hu, and Kai Hu "Image fusion method based on regional feature and improved bidimensional empirical mode decomposition," Journal of Electronic Imaging 27(1), 013017 (12 February 2018). https://doi.org/10.1117/1.JEI.27.1.013017

Received: 3 August 2017; Accepted: 9 January 2018; Published: 12 February 2018

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