Camera motion deblurring is an important low-level vision task for achieving better imaging quality. When a scene has outliers such as saturated pixels, the captured blurred image becomes more difficult to restore. In this paper, we propose a novel method to handle camera motion blur with outliers. We first propose an edge-aware scale-recurrent network (EASRN) to conduct deblurring. EASRN has a separate deblurring module that removes blur at multiple scales and an upsampling module that fuses different input scales. Then a salient edge detection network is proposed to supervise the training process and constraint the edges restoration. By simulating camera motion and adding various light sources, we can generate blurred images with saturation cutoff. Using the proposed data generation method, our network can learn to deal with outliers effectively. We evaluate our method on public test datasets including the GoPro dataset, Kohler's dataset and Lai's dataset. Both objective evaluation indexes and subjective visualization show that our method results in better deblurring quality than other state-of-the-art approaches.

中文翻译：

---

超越相机运动模糊去除：如何处理去模糊中的异常值

相机运动去模糊是一项重要的低级视觉任务，可实现更好的成像质量。当场景具有异常值（例如饱和像素）时，捕获的模糊图像变得更难以恢复。在本文中，我们提出了一种处理带有异常值的相机运动模糊的新方法。我们首先提出了一个边缘感知尺度循环网络（EASRN）来进行去模糊。EASRN 有一个单独的去模糊模块，可以在多个尺度上去除模糊，还有一个上采样模块，可以融合不同的输入尺度。然后提出了一个显着边缘检测网络来监督训练过程并约束边缘恢复。通过模拟相机运动并添加各种光源，我们可以生成饱和度截止的模糊图像。使用所提出的数据生成方法，我们的网络可以学习有效地处理异常值。我们在公共测试数据集上评估我们的方法，包括 GoPro 数据集、Kohler 数据集和 Lai 数据集。客观评价指标和主观可视化都表明，我们的方法比其他最先进的方法具有更好的去模糊质量。