# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 1 Introduction  
Limited on the performance of capture devices and environmental conditions, images captured in a real dynamic scene are often blurry. Blur is one of the main degrading factors in the captured images. Blurry image not only affects subjective perception, but also affects the performance of the subsequent intelligent analysis. Insufficient camera resolution, long shooting distance, camera shake, and other factors may result in global image blur, while target motion, scene depth changes, and out-of-focus issues will lead to local blur. Different types of blur are randomly and complicatedly coupled. Therefore, the restoration of non-uniform blurry images in real dynamic scenes is a very challenging issue in low-level computer vision. Due to the ill-posed essence of the image restoration problem, the conventional blind restoration methods [1; 2; 3; 4; 5; 6; 7] usually make assumptions about the blur kernel and then use the prior of natural images to restore the clear images. Most conventional methods [8; 9; 10; 11; 12] mainly focus on solving the motion blur caused by simple target movement, camera translation, rotation, and other factors. It is still difficult to handle the blurred images in real dynamic scenes. Early learning-based blind image restoration methods [13; 14; 15; 16] used convolutional neural networks (CNNs) to estimate unknown blur kernels, such methods are difficult to deal with blur in a dynamic scene. Existing methods [17; 18; 19; 20; 21] adopt end-to-end deep networks to directly learn the mapping relationship between the blurry and clear images. This kind of method takes less consideration on the characteristics of the blurry images, which often leads to over smoothing. According to whether the blur kernel is spatially varying, the blurry image is divided into the global blur and local blur [16], or called uniform blur and non-uniform blur. We have observed that real blurry images are often formed by the coupling of these two typesof blur. The background region usually appears global blur, or the change of the blur kernel is relatively gentle, while the foreground moving targets are local blur. The global blur appears on the whole image, while the local blur kernels may vary with respect to spatial position [22]. Accurately predicting the non-uniform blur area is of great significance to improve the quality of the blind restored images. Only a few methods [16; 23] classified blur regions according to the varying of blur. [PERSON] et al. [23] proposed a Bayesian classifier to classify image patches into blurry and non-blurry patches. [PERSON] et al. [16] proposed a Fourier transform-based blur kernel estimation prior. This method adopted the frequency domain characteristics and a deep neural network to solve the patch-based image deblurring. Although these two methods [16; 23] can detect the local blur of real images, these methods only extract hand-craft features. Due to the diversity of dynamic scene blur, the performances of these methods are still limited. Reference [24] proposed a multi-branch network that can perceive human movement and achieved good results. However, because this method only considers the movement of people in the foreground, the applicability is still limited. We designed a dual-branch network to resolve the problem of non-uniform blur image restoration. A phased training strategy is designed to disentangle the global and local blur features, and an attention mechanism is used to fuse the two kinds of features, thereby improving the quality of blind non-uniform blurry image restoration. Figure 1 shows the visualization of feature maps extracted via the global and local blur feature extraction branches, respectively. We can observe that the dual-branch network can capture the global and local blur features efficiently. Different from other multi-branch networks, in our model, two branches are designed to the same architecture. We show that the disentangling function can be achieved via a merely data-driven manner by a phased training strategy.