Image super-resolution (SR) refers to the reconstruction of a high-resolution (HR) image from single or multiple observed degraded low-resolution (LR) images for the purpose of improving image's visual effects and getting more available information. We propose an image super-resolution algorithm based on collaborative representation and clustering in this paper. In the training stage, the image samples are clustered according to the image features and multiple dictionaries are trained by using the differences of image features, which overcomes the shortcoming of lack of expressiveness of traditional single-dictionary training methods. Moreover, projection matrices between different HR and LR image clustering are computed via collaborative representation, which accelerate the speed of image reconstruction. Experiments demonstrate that compared with other methods, the proposed method not only enhanced PSNR and SSIM metrics for reconstructed images but also improved image's visual effects.