中文摘要：

现有的非局部稀疏表示去噪算法大多严格依赖于块匹配,且其去噪性能受制于匹配的相似块的数量.鉴于此,提出了组约束与非局部稀疏的图像去噪模型.模型在非局部稀疏的基础上加入了分组约束,增强了图像块之间的非局部相似度,块匹配更加精确.实验表明,模型无论是在视觉效果还是峰值信噪比上均具有较好的性能.

英文摘要：

The most existing denoising algorithms based on nonlocal sparse representation are strictly dependent on patch matching,and the denoising performance is subject to the numbers of similar patches.So a image denoising algorithm based on nonlocally sparse representation and group is proposed.The group-based constraints is introduced to the nonlocal sparse representation,which can enhance the nonlocal similarity between image patches and the patch matching is more accurate.Experiments show that the model has a good performance in both visual effect and peak signal to noise ratio.