Dense Block U-net for Dynamic Scene Deblurring
Yujie Wu, Hong Zhang, Yawei Li, Yinan Mao, Lei He, Zhoufeng Liu · 2020
Motion blur often arises in a single image because of the camera shake, the objects motion and the depth variation. The image deblurring is a challenging task due to its ill-posed nature. To remove these blurriness, the conventional energy optimization based methods always rely on the assumption such that the blur kernel is uniform across the entire image. With the development of the deep neural network, the learning based methods were proposed to tackle with the non-uniform blur cases. In this paper, we propose a U-Net network containing dense blocks for dynamic scene deblurring. By passing the kernel estimation, our model significantly reduces the inference time. The extensive experiments on both synthetic and real blurred images demonstrate that our method outperforms the state-of-the-art blind deblurring algorithms.