Medical image processing is an important and key problem in image processing. High-resolution images with abundant details contribute to assisting physicians and computer aided diagnosis programs. According to the characteristics of magnetic resonance images, we propose a single-frame super-resolution reconstruction method based on wavelet features and clustered dictionaries. In the training phase, the multiscale wavelet features of low-resolution images and all high-frequency components of high-resolution images are extracted, and all of these feature images are overlapping and separated into patches. Then, K-means algorithm is used to cluster feature patches into several classes, for each class a pair of dictionaries is learned using K-singular value decomposition. In the reconstruction phase, each low-resolution patch is classified and sparsely represented with its corresponding dictionary atoms. Iterative back projection is used for post-processing to further improve the reconstruction quality. Experimental results show that the proposed method outperforms other main-stream methods, both visually and quantitatively.