| Abstract: | Medical image fusion is a process by which two different models of images are combined into a single image, in order to provide doctors with accurate diagnoses, and take right action. This paper proposes an image fusion method based on sparse representation with KSVD. Firstly, all source images are combined into a joint-matrix, which can be represented with sparse coefficients using an overcompletedictionary trained by KSVD algorithm. Secondly, the coefficients which are considered as image features are combined with the choose-max fusion rule. Finally, the fused image is reconstructed from the concatenated coefficients and the overcomplete dictionary. Compared with three state-of-the-art algorithms, the proposed method has better fusion performance. |
