In order to address the limitations of single modal brain images and further hig hlight detail features and enhance visual effects,an algorithm framework based on mu lti-scale edge preserving decomposition and improved sparse representation (ISR) is proposed.First, the source image is decomposed to obtain high frequency and low frequency subbands.Secondly,an impr oved sparse representation with multi-norm weighted metric is used to fuse low-frequency s ubbands,and an improved guide filter with multi-scale morphological gradient (MSMG) is used to remove details.At the same time,the simplified pulse-coupled neural network fuses its high frequency subb ands.Finally,the inverse transformation yields the fused brain image.Experimental results show that this paper has significant advantages in the protection of edge information,improvement of fus ion efficiency and saving of time cost.