The method of blind deblurring based on machine learning can effectively deal with the blurred images in the real world. However, existing multi-level architectures can lead to problems such as the inability to reserve edges, the expected introduction of artifacts and ghosts when deblurring. Multi methods have found that using $L_0$ norm to realize image sparse representation can help keeping main structure of images. In this paper, an edge extraction module based on $L_0$ sparse representation is proposed to preserve the edge of images, which is embedded in a multi-scale recurrent network(SRN). When the current scale transmits information to the next scale, edge enhancement is performed using the edge extraction module. Furthermore, considering the correlation among pixels and the correlation among channels, we introduce dual-attention mechanism into the encoder-decoder structure. The deblurring experiment was carried out on the GOPRO dataset. Comparing with 5 state-of-art methods qualitatively and quantitatively, the experimental results show that the proposed method can better preserve the image edges and effectively avoid the artifact of the image. And the peak signal-to-noise ratio of the proposed method are improved compared with other methods.

基于机器学习的盲去模糊方法可以有效处理现实世界中的模糊图像。但是，现有的多级体系结构可能会导致诸如无法保留边缘，在进行模糊处理时预期会引入伪影和幻影之类的问题。多种方法发现使用${\mathrm{大号}}_0$实现图像稀疏表示的规范可以帮助保持图像的主要结构。本文提出了一种基于边缘提取的模块${\mathrm{大号}}_0$提出了一种稀疏表示来保留图像的边缘，该图像嵌入在多尺度递归网络（SRN）中。当当前比例尺将信息传输到下一个比例尺时，将使用边缘提取模块执行边缘增强。此外，考虑到像素之间的相关性和通道之间的相关性，我们在编码器-解码器结构中引入了双注意机制。去模糊实验是在GOPRO数据集上进行的。定性和定量地与5种最新方法进行比较，实验结果表明，该方法可以更好地保留图像边缘并有效避免图像的伪影。与其他方法相比，该方法的峰值信噪比有所提高。