In order to overcome the problems that the dictionary training process is time-consuming and the reconstruction quality couldn't meet the applications, we propose a super resolution reconstruction algorithm which based on a supervised KSVD multi-dictionary learning and class-anchored neighborhood regression. Firstly, the Gaussian mixture model clustering algorithm is employed to cluster the low resolution training features; Then we use the supervised KSVD algorithm to generate each subclass dictionary and a discriminative-linear classifier simultaneously; Finally, each input feature block is categorized by the classifier and reconstructed by the corresponding subclass dictionary and class-anchored neighborhood regression. Experimental results show that our method obtains a better result both on subjective and objective compare with other methods, and has a better adaptability to face image.