  Multimodal image fusion aims to combine relevant information from images
acquired with different sensors. In medical imaging, fused images play an
essential role in both standard and automated diagnosis. In this paper, we
propose a novel multimodal image fusion method based on coupled dictionary
learning. The proposed method is general and can be employed for different
medical imaging modalities. Unlike many current medical fusion methods, the
proposed approach does not suffer from intensity attenuation nor loss of
critical information. Specifically, the images to be fused are decomposed into
coupled and independent components estimated using sparse representations with
identical supports and a Pearson correlation constraint, respectively. An
alternating minimization algorithm is designed to solve the resulting
optimization problem. The final fusion step uses the max-absolute-value rule.
Experiments are conducted using various pairs of multimodal inputs, including
real MR-CT and MR-PET images. The resulting performance and execution times
show the competitiveness of the proposed method in comparison with
state-of-the-art medical image fusion methods.
