In order to address the limitations of single modal brain images and further highlight detail features and enhance visual effects,an algorithm framework based on multi-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 improved sparse representation with multi-norm weighted metric is used to fuse low-frequency subbands,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 subbands.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 fusion efficiency and saving of time cost.