Infrared polarization and intensity image fusion based on bivariate BEMD and sparse representation

The issue of infrared polarization and intensity images fusion has shown important value in both military and civilian areas. In this paper, a novel fusion approach is addressed by reasonably integrating the common and innovation features between the above two patterns of images, employing Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) together. Firstly, the high and low frequency components of source images are separated by B-BEMD, and the max-absolute rule is used as the activity level measurement to merge the high frequency components in order to effectively retain the details of the source images. Then, the common and innovation features between low frequency components are extracted by the tactfully designed SR-based method, and are combined respectively by the proper fusion rules for the sake of highlighting the common features and reserving the innovation features. Finally, the inverse B-BEMD is performed to reconstruct the fused image. Experimental results indicate the effectiveness of the proposed algorithm compared with traditional MST-and SR-based methods in both aspects of subjective visual and objective performance.

Automated summary

A novel fusion approach combining Bivariate Bidimensional Empirical Mode Decomposition (B-BEMD) and Sparse Representation (SR) is proposed in this paper, effectively highlighting common features and preserving innovative features while retaining source image details. The experimental results show the effectiveness of the proposed algorithm compared to traditional methods in terms of subjective visual and objective performance.