本文提出一种基于稀疏字典编码的超分辨率方法。该方法有效地建立高、低分辨率图像高频块间的稀疏关联，并将这种关联作为先验知识来指导基于稀疏字典的超分辨率重建。较超完备字典，稀疏字典对先验知识的表达更紧凑、更高效。字典训练过程中，本文选用高频信息作为高分辨率图像的特征，更有效地建立高、低分辨率图像块间的稀疏关联，所需的训练样本更少。优化方法采用稀疏K-SVD 算法以提高稀疏字典编码的计算效率。采用自然图像进行实验，与其它基于学习的超分辨率算法相比，重建图像的质量更优。

A super-resolution method based on sparse dictionary is presented. The method efficiently builds sparse association between high-frequency components of HR image patches and LR image feature patches, and defines the association as a prior knowledge to guide super-resolution reconstruction based on sparse dictionary. Compared with overcomplete dictionary, sparse dictionary is more compact and effective to express the prior knowledge. We choose the high-frequency component of the HR image patch as its feature for dictionary training, which builds the sparse association between LR image patches and HR ones with better efficiency and less training examples. Sparse K-SVD algorithm is adopted as optimization method to improve the computation efficiency. Experiments with natural images show that our method outperforms several other learning-based super-resolution algorithms.