# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 2 Related Work  
### Image Deblurring for Dynamic Scene  
Complex non-uniform blur results from camera shaking, object motion, varying in depth, and defocus, which make it a daunting task in computer vision. Conventional deblurring methods make efforts to estimate the blur kernel corresponding to each pixel, which is a serious ill-conditioned problem. They usually make some assumptions about  
Figure 1: The feature map of disentangle branches for local and global blur feature maps. From left to right are the blurry image, local blur feature map, and global blur feature map.  
blur kernels. Methods [2; 6; 25] used a simple parameter prior model to quickly estimate local linear blur kernel. In Reference [7; 26], different parameter prior models are employed to estimate blur kernel and restore images iteratively. However, most conventional methods [8; 9; 10; 11; 12] mainly focuses on solving the motion blur caused by simple target motion, camera translation, rotation, and other factors, while the blurry image of a real dynamic scene suffers from complex and non-uniform blurry degradation. Therefore, conventional methods are difficult to effectively solve the problem of non-uniform blur in real scenes. They often involve iteration, which results in time-consuming and limited performance.  
Early learning-based methods [13; 14; 15; 16] mainly used CNN to estimate unknown blur kernels to improve the accuracy in blind restoration, and then conventional deconvolution methods are employed to restore the blurry images. [PERSON] et al. [13] and [PERSON] et al. [16] parameterized and estimated the blur kernel through classification and regression analysis. These methods [13; 14; 15; 16] improve the conventional deblurring framework with CNN kernel estimation. The quality of image restoration depends on the accuracy of estimated blur kernels.  
Recently, some end-to-end deep learning-based deblurring methods [17; 18; 19; 20; 21] have emerged, inspired by research work, such as image transfer-based on Generative Adversarial Network (GAN) [27]. [PERSON] et al. [20] regarded deblurring as a special case of image style transfer, that is, CNN is used to model the mappings from the blurry to clean image. GAN is used to generate images that are close to the real clear images. [PERSON] et al. [18] designed a multi-scale network to extract the multi-scale information of the image in an iterative manner, and gradually restore the clear image. [PERSON] et al. [28] proposed a scale recursive network with shared parameters. The experimental results show that these methods have achieved good results in both of the subjective objective quality compared with the conventional methods.  
However, most of the above-mentioned methods pay less attention to the characteristics of the blurry images. We believe that an adaptive mechanism is necessary to handle the non-uniform spatial varying blur kernels. In order to adaptively restore different kinds of blur in the network, this paper disentangles the real complex blur image into near uniform global blur and non-uniform local blur. A dual-branch network with attention mechanism is introduced to disentangle the global-local blur features and reconstruct the real blurring image adaptively.