针对红外图像成像质量不高、分辨率低的问题，提出稀疏表示超分辨率重建技术。选用不同方向的Sobel算子对样本低分辨率图像进行特征提取，利用提取的特征图像训练高、低分辨率字典。使用同样的方法获得低分辨率目标图像的特征图像，结合低分辨率字典求得目标图像的稀疏系数。依据高、低分辨率图像的结构相似性，通过稀疏系数和高分辨率字典得到重建的高分辨率图像。经过实验验证，该方法能取得较好的超分辨率重建效果。

To solve the problems of low quality and poor resolution of infrared images, super-resolution reconstruction based on sparse representation is put forward. First of all, Sobel operators of different directions are applied to extract the feature of low-resolution images, and the high and low-resolution dictionaries are trained by using the extracted feature images. Then the same way is used to obtain the feature image of low-resolution target image. The sparse coefficient of the target image is obtained through low-resolution dictionary and the feature image. Finally, according to the structural similarity between high and low-resolution images, the high-resolution image can be reconstructed by using the sparse coefficient and high-resolution dictionary. The experiments prove that this method can get a better super-resolution reconstruction effect.