Aiming at the problems of artifacts and loss of details in multimodal medical fusion, a two-scale multimodal medical image fusion method framework using multiscale edge-preserving decomposition and sparse representation was proposed. Firstly, the source image was decomposed at multiple scales by utilizing an edge-preserving filter to obtain the smoothing and detail layers of the source image. Then, an improved sparse representation fusion algorithm was employed to fuse the smoothing layers, and on this basis, an image block selection based strategy was proposed to construct the dataset of the over-complete dictionary and the dictionary learning algorithm was used for training the joint dictionary, as well as a novel multi-norm based activity level measurement method was introduced to select the sparse coefficients; the detail layers were merged by an adaptive weighted local regional energy fusion rule. Finally, the fused smoothing layer and detail layers were reconstructed with multi-scale to obtain the fused image. Comparison experiments were conducted on the medical images from three different imaging modalities. The results demonstrate that the proposed method preserves more salient edge features with the improvement of contrast and has advantages in both visual effect and objective evaluation compared to other multi-scale transform and sparse representation methods.