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.

中文翻译：

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用于单幅图像去模糊的深度光谱空间网络

受到深度神经网络在计算机视觉各个领域取得巨大成功的启发，最近几天，对图像去模糊的研究开始变得更加活跃。然而，大多数先前的方法通常无法准确去除恢复结果中的模糊伪影，例如对象边界处的重影效果和局部细节的退化。在本文中，我们提出了一种深度光谱空间网络（DSSN）来解决单幅图像去模糊问题。具体来说，所提出的方法能够通过频谱恢复器最小化模糊输入和相应清晰图像之间的频率幅度差异，以全局方式有效地恢复场景特征，空间恢复器微调中间结果的局部细节，这是由光谱估计，基于强度相似度。这种去模糊过程的级联方案非常适合以粗到细的方式清晰地恢复边缘状结构以及纹理信息。基准数据集的实验结果表明，所提出的 DSSN 优于最先进的方法。代码和模型公开在：https://github.com/SeokjaeLIM/DSSN_release。