A novel multi-modality image fusion method based on image decomposition and sparse representation

Abstract Multi-modality image fusion is an effective technique to fuse the complementary information from multi-modality images into an integrated image. The additional information can not only enhance visibility to human eyes, but also mutually complement the limitations of each image. To preserve the structure information and perform the detailed information of source images, a novel image fusion scheme based on image cartoon-texture decomposition and sparse representation is proposed. In proposed image fusion method, source multi-modality images are decomposed into cartoon and texture components. For cartoon components a proper spatial-based method is presented for morphological structure preservation. An energy based fusion rule is used to preserve structure information of each source image. For texture components, a sparse-representation based method is proposed. A dictionary with strong representation ability is trained for the proposed sparse-representation based fusion method. Finally, according to the texture enhancement fusion rule, the fused cartoon and texture components are integrated. The experimentation results have clearly shown that the proposed method outperforms the state-of-art methods, in terms of visual and quantitative evaluations.