图像超分辨率重建是利用单幅或多幅降质的低分辨率图像重建得到高分辨率图像，以提高图像的视觉效果并获得更多可用的信息。本文提出结合图像特征聚类和协同表示的超分辨率重建方法。在训练阶段根据图像的特征信息对图像样本进行聚类并利用图像特征的差异性训练不同的字典，克服了传统训练单个字典方法对图像特征表示不足的缺点。而且利用协同表示方法求得不同聚类的高、低分辨率图像样本之间的映射矩阵，提高了图像重建速度。实验表明，本文方法与其他方法相比，不仅提高了重建图像的PSNR和SSIM指标，而且改善了视觉效果。

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.