强边缘提取网络用于非均匀运动模糊图像盲复原

基于深度学习的非均匀运动图像去模糊方法已经获得了较好的效果. 然而, 现有的方法通常存在对边缘恢复不清晰的问题. 因此, 本文提出一种强边缘提取网络(Strong-edge extraction network, SEEN), 用于提取非均匀运动模糊图像的强边缘以提高图像边缘复原质量. 设计的强边缘提取网络由两个子网络SEEN-1和SEEN-2组成, SEEN-1实现双边滤波器的功能, 用于提取滤除了细节信息后的图像边缘. SEEN-2实现L₀平滑滤波器的功能, 用于提取模糊图像的强边缘. 本文还将对应网络层提取的强边缘特征图与模糊特征图叠加, 进一步利用强边缘特征. 最后, 本文在GoPro数据集上进行了验证实验, 结果表明: 本文提出的网络可以较好地提取非均匀运动模糊图像的强边缘, 复原图像在客观和主观上都可以达到较好的效果.

Strong Edge Extraction Network for Non-uniform Blind Motion Image Deblurring

Although non-uniform motion image deblurring based on the deep learning has achieved better recovery effect, the most of the existing methods cannot recover the image edge well. In this paper, a strong edge extraction network (SEEN) is proposed for extracting the strong edges of the non-uniform motion blurry image to improve the quality of image deblurring. The designed SEEN is composed of two sub-networks, that is, SEEN-1 and SEEN-2. SEEN-1 is designed as a bilateral filter for extracting the edges of the image after filtering the image details. SEEN-2 is designed as an L₀ smoothing filter for extracting strong edges of the blurry image. Meanwhile, we also combine the strong edge features map and the blurry features map for further using the strong edge features. Finally, some experiments are executed on GoPro dataset and the results demonstrate that the proposed network can better extract the strong edge of the non-uniform motion blurry image, and obtain good results in both quality of visual perception and quantitative measurement.