针对红外云图分辨率低、视觉效果较差的问题，提出一种图像结构组稀疏表示的超分辨率方法。该方法充分利用红外云图的结构自相似性，将结构组作为稀疏表示的基本单位，建立图像结构组稀疏表示模型。在训练字典过程中通过高斯混合模型学习图像结构组的先验信息，再对样本块进行聚类，利用主成分分析学习得到紧凑的分类字典。在重建阶段对每个结构组自适应选取最匹配的字典，使用改进了的加权l1 范数优化方法求解稀疏系数。实验结果表明，与ScSR、Zeyde、NARM 等算法相比，所提算法在视觉效果以及图像质量评价指标上均有所提高，红外云图重构质量有较为明显的改善。

For the problems of low-resolution and poor visual effect of infrared cloud images, a super-resolution method based on structural group sparse representation was proposed. In consideration of the self similarity of infrared image, a structural group sparse representation model was first established. In the training stage, the Gauss mixture model is used to study the prior information of the image structure group, and then to cluster it, using principal component analysis to get a compact classification dictionary. In the reconstruction phase, the best matching dictionary of each structure group is selected, adaptively reweighted l1-norm sparsity is introduced to effectively obtain sparse coefficient. Experimental results demonstrate that our method can achieve better reconstruction effect in both subjective visual effect and objective evaluation criteria compared with ScSR, Zeyde and NARM methods．