A new infrared image fusion method using empirical mode decomposition and inpainting

This paper puts forward a new method to fuse infrared images using empirical mode decomposition (EMD) and inpainting algorithms. EMD is a non-parametric, data-driven analysis tool that decomposes non-linear, non-stationary signals into a set of signals denominated intrinsic mode functions (IMFs) and a residual. Fusion rules are set up to fuse the corresponding IMFs and residual by designing for the weighting factor to emphasize desirable features of the original images. The image is then reconstructed using fused IMFs and residuals. This new image fusion algorithm is evaluated based on several tests such as edge information, mutual information, and information entropy. Test results show that the proposed method is effective when fusing infrared images, as the fused images are very clear and include rich information from the original sources.