Improving Performance of ICA Domain Multimodal Image Fusion in Presence of Noise

In this paper we present a novel fusion method in ICA domain. The method uses nonlinear shrinkage of the coefficients in ICA domain to determine the ICA coefficients to be used in the fused image reconstruction, so that the noise transferred from input images into the fused output is minimized. Experimental results have shown that it has improved performance in the presence of noise, compared to the basic ICA algorithm. Compared to standard multiresolutional fusion methods, the noise in the fused image is visually less annoying, while the important detail is still adequately represented. The proposed method exhibits considerably higher fusion performance, measured by Piella and Petrovic fusion metric, than the basic ICA algorithm and improvement over other state-of-the-art algorithms.