Deep Spectral-Spatial Network for Single Image Deblurring

Inspired by the great success of the deep neural networks in various fields of computer vision, studies for image deblurring have begun to become more active in recent days. However, most previous approaches often fail to accurately remove the blur artifacts, e.g., ghosting effects at the object boundaries and degradation of local details, in restored results. In this paper, we propose a deep spectral-spatial network (DSSN) for resolving the problem of single image deblurring. Specifically, the proposed method is able to efficiently recover scene characteristics in a global manner by minimizing differences of the frequency magnitude between the blurred input and corresponding sharp image via the spectral restorer, and the spatial restorer fine-tunes local details of the intermediate result, which is estimated by the spectral one, based on the intensity similarity. This cascaded scheme of deblurring processes is fairly desirable for clearly restoring edge-like structures as well as the textural information in a coarse-to-fine manner. Experimental results on benchmark datasets demonstrate that the proposed DSSN outperforms state-of-the-art methods. The code and model are publicly available at: https://github.com/SeokjaeLIM/DSSN_release.