IEEE Trans Image Process. 2018 Jan;27(1):194-205. doi: 10.1109/TIP.2017.2753658. Epub 2017 Sep 18.
The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images. Motivated by the success of the existing filtering-based deblurring methods, the proposed model consists of two stages: suppressing extraneous details and enhancing sharp edges. We show that the two-stage model simplifies the learning process and effectively restores sharp edges. Facilitated by the learned sharp edges, the proposed deblurring algorithm does not require any coarse-to-fine strategy or edge selection, thereby significantly simplifying kernel estimation and reducing computation load. Extensive experimental results on challenging blurry images demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of visual quality and run-time.
最先进的去模糊方法的成功主要取决于在从粗到精的核估计过程中恢复清晰边缘。在本文中，我们提出从模糊图像中提取清晰边缘的深度卷积神经网络。受现有基于滤波的去模糊方法的成功启发，所提出的模型由两个阶段组成：抑制多余细节和增强清晰边缘。我们表明，两阶段模型简化了学习过程并有效地恢复了清晰边缘。在学习到的清晰边缘的帮助下，所提出的去模糊算法不需要任何粗到精的策略或边缘选择，从而大大简化了核估计并减少了计算负载。在具有挑战性的模糊图像上的广泛实验结果表明，所提出的算法在视觉质量和运行时间方面都优于最先进的方法，无论是在合成图像还是真实世界图像上。
