Aiming at solving the defect, slow speed of magnetic resonance imaging (MRI), we propose a magnetic resonance image reconstruction method based on compressed sensing (CS), combining Shift-Invariant Discrete Wavelet Transformation (SIDWT) and dictionary learning (DL). Since the method is based on CS, it obtains the part of k-space image data by undersampling and reconstructs the original image by SIDWT. Then the dictionary learning and sparse coding phase are used in the sparse representation of the original image. Finally, the undersampling image is reconstructed by image iteration update stage, and the quality of the reconstructed image is evaluated objectively via the Peak Signal-to-Noise Ratio (PSNR). Compared with the existing sparse reconstruction algorithm, the proposed method achieves high quality reconstruction of undersampled MR images, and shortens the time of MR image signal acquisition.