In the field of clinical medicine, image aided diagnosis has a high demand for the effect of medical view processing. To solve the problem of poor visual effect in medical image fusion, a medical image fusion algorithm based on sparse theory and fast finite shear transform is proposed, which improves the efficiency of medical image processing. Firstly, Fast Finite Shear wave Transform (FFST) is used to decompose the source image into high-frequency coefficients and low-frequency coefficients. Secondly, according to the different properties of high-frequency coefficients and low-frequency coefficients, different fusion strategies are provided, and the high-frequency coefficients are processed by the relative standard deviation comparison method. For the low-frequency coefficients with poor sparsity, K-SVD method is used to train, and a dictionary is obtained Finally, the high-frequency and low-frequency coefficients after fusion are fused into the medical image by FFST inverse transform. The experimental results show that the image fusion effect of the algorithm is good, especially in improving the image clarity and so on, which has sound practical value and application prospects.