For the image restoration problem, recent variational approaches exploiting nonlocal information of an image have demonstrated significant improvements compared with traditional methods utilizing local features. Hence, we propose a new variational model based on the sparse representation of image groups, to recover blurred images with Cauchy noise. To achieve efficient and stable performance, an alternating optimization scheme with a novel initialization technique is used. Experimental results suggest that the proposed method outperforms other methods in terms of both visual perception and numerical indexes.

中文翻译：

---

用稀疏噪声还原模糊图像的组稀疏表示

对于图像恢复问题，与利用局部特征的传统方法相比，利用图像非局部信息的最新变型方法已显示出显着改进。因此，我们提出了一种基于图像组稀疏表示的新变分模型，以恢复柯西噪声下的模糊图像。为了获得有效和稳定的性能，使用了一种具有新颖初始化技术的交替优化方案。实验结果表明，该方法在视觉感知和数字指标方面均优于其他方法。