Single image blind motion deblurring refers to transferring a blurred motion image into a corresponding clear image, which is a challenging and classic problem in the field of computer vision. The spatially variant blur is usually caused by many factors, such as camera jitter and object motion. Since image deblurring can be regarded as the task of image transformation, deep learning methods based on coarse-to-fine scheme, especially those using multi-scale architectures become popular. However, they have the disadvantages of unsatisfactory image quality and time-consuming running caused by large kernel size. In this paper, we propose a novel end-to-end network structure based on Deep Hierarchical Multi-patch network architecture integrated with Context Module and additional ResBlocks in order to tackle deblurring problem. Compared with recently proposed networks, it generates images with better visual effect as well as higher image quality index. Besides, our model significantly reduces the test time. We evaluate the proposed network structure on public GoPro dataset, a large-scale image dataset with complex synthetic blur. The experiments on the benchmark dataset prove that our effective method outperforms other state-of-the-art blind deblurring algorithms both qualitatively and quantitatively, which demonstrates the effectiveness of Context Module in the task of single image blur removal.
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