In order to overcome the weak of the limit ability of preservation of edges and easy to produce visual artifacts in some super-resolution methods based on dictionary learning, we propose multi-dictionary learning imagesuper-resolution method with edge-enhanced, which can effectively restore the image edge details. Firstly, the training image patches will be classified by using K-means, and then quickly learn multi-dictionary pairs by employing the Boost K-SVD algorithm. During the super-resolution reconstruction, the method adaptively selects the optimal dictionary pairs for sparse decomposition and recovery. To improve the visual quality of edge after image reconstruction, we employed direction-preserving regularization according to the input test low-resolution (LR) image, meanwhile learning the natural image database edge sharpness statistics prior to constraint the image reconstruction of edges. The experimental results demonstrate the effectiveness of the proposed algorithm.