In SCSR (sparse coding sparse representation) algorithm, the universal overcompleted dictionary cannot be adapted to variety types of images and too much redundancy is introduced by global sparse reconstruction. To overcome these shortcomings，the SR algorithm based on MCA (morphological component analysis) decomposition and sample clustering is proposed. Firstly, the training feature patchs are clustered by Kmeans algorithm, and then each clustering is trained to get dictionaries, which are used to process variety types of images. Secondly, the image is decomposed into texture component and smooth component by MCA method. The texture component is reconstructed sparsely and the smooth component is enlarged by Bicubic algorithm. Finally, compared with other SR methods, this algorithm can restore the image edge details better.