Image deblurring is a challenging field in computational photography and computer vision. In the deep learning era, deblurring methods boosted with neural networks achieve significant results. However, the existing methods mainly focus on solving specific image deblurring problem, and overlook the origin of the motion blur. In this paper, we revisit how blur occurs, and divide them into three categories, i.e. caused by relative motion between camera and scene, caused by the movement of the object itself and the edges of a blurring image, which may meet discontinuity because of the pixels trajectory sampled outside the image. To address the issues of different blurs in an image, we propose a two-stage neural network for image deblurring named RAID-Net. In order to remove the global blurry region caused by camera movements, we first use a U-shape network to get the coarse deblurred image. Then we leverage an adaptive reasoning module to model the relationship between different blurry regions within one image jointly and remove the other two categories of motion blur. Experiments on two public benchmark datasets demonstrate that our method achieves comparable or better results over the state-of-the-art methods.

中文翻译：

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领导跟随二阶非线性多智能体系统的事件触发有限时间滑模控制


图像去模糊是计算摄影和计算机视觉中一个具有挑战性的领域。在深度学习时代，神经网络增强的去模糊方法取得了显着的效果。然而，现有的方法主要集中于解决特定的图像去模糊问题，而忽视了运动模糊的根源。在本文中，我们重新审视模糊是如何发生的，并将其分为三类，即由相机和场景之间的相对运动引起的、由物体本身的运动引起的以及模糊图像的边缘引起的，这可能会遇到不连续性，因为在图像外部采样的像素轨迹。为了解决图像中不同模糊的问题，我们提出了一种用于图像去模糊的两阶段神经网络，名为 RAID-Net。为了去除相机运动引起的全局模糊区域，我们首先使用 U 形网络来获得粗略的去模糊图像。然后，我们利用自适应推理模块对一幅图像中不同模糊区域之间的关系进行联合建模，并消除其他两类运动模糊。对两个公共基准数据集的实验表明，我们的方法与最先进的方法相比取得了可比或更好的结果。