Abstract: Magnetic resonance imaging (MRI) provides rich pathological information which is of great significance in diagnosis and treatment of brain lesions. High resolution MRI images are hard to obtain in clinic due to the limitations of sampling time and existing medical equipment. To address these problems, a super-resolution(SR) reconstruction method is proposed based on differential curvature grouping mixture model. Firstly, a differential curvature algorithm is introduced on the basis of gradient feature extraction to detect edges, slopes and other feature structures of the image. Following that, the feature blocks are divided into three groups including smooth, texture and edge regions. Secondly, the student t-distribution mixed model is applied to learn the model parameters of the three sets of feature regions. Finally, multiple distribution models with larger likelihood probability are selected to reconstruct high-resolution image patches. The experiments on the cancer imaging archive (TCIA) dataset show that this method achieves an average peak signal-to-noise ratio (PSNR) of 41.36 dB, 35.01 dB and 31.32 dB with an average structural similarity index (SSIM) of 0.984 8, 0.941 5 and 0.879 5 respectively for ×2,×3 and×4 SR. Compared with some current SR reconstruction approaches, the proposed method reconstructs more reasonable images at the cost of less time with richer texture details and clearer edges.