Attention Network for Non-Uniform Deblurring
Qing Qi, Jichang Guo, Weipei Jin
Abstract
Recently, image deblurring task is valuable and challenging in computer vision. However, existing learning-based methods can not produce satisfactory results, such as lacking of salient structures and fine details. In this paper, we propose a solution to transform spatially variant blurry images into the photo-realistic sharp manifold. In this paper, we investigate an attention network for image deblurring. Instead of relying on local receptive fields to construct features by previous state-of-the-art methods, the non-local features for capturing long-range dependencies and the local features rely on receptive fields should be jointly considered. Therefore, we propose a novel dense feature fusion block that consists of a channel attention module and a pixel attention module. In addition, we further densely connected multiple dense feature fusion blocks to acquire high-order feature representation. Moreover, a scale attention module is further introduced for removing unfavorable features while retaining features that facilitate image recovery. Comprehensive experimental results show that our method is able to generate photo-realistic sharp images from real-world blurring images and outperforms state-of-the-art methods.