A significant volume of analog information, i.e., documents and images, have been digitized in the form of scanned copies for storing, sharing, and/or analyzing in the digital world. However, the quality of such contents is severely degraded by various distortions caused by printing, storing, and scanning processes in the physical world. Although restoring high-quality content from scanned copies has become an indispensable task for many products, it has not been systematically explored, and to the best of our knowledge, no public datasets are available. In this paper, we define this problem as Descanning and introduce a new high-quality and large-scale dataset named DESCAN-18K. It contains 18K pairs of original and scanned images collected in the wild containing multiple complex degradations. In order to eliminate such complex degradations, we propose a new image restoration model called DescanDiffusion consisting of a color encoder that corrects the global color degradation and a conditional denoising diffusion probabilistic model (DDPM) that removes local degradations. To further improve the generalization ability of DescanDiffusion, we also design a synthetic data generation scheme by reproducing prominent degradations in scanned images. We demonstrate that our DescanDiffusion outperforms other baselines including commercial restoration products, objectively and subjectively, via comprehensive experiments and analyses.

翻译：大量的模拟信息（如文档和图像）已以扫描副本的形式数字化，以便在数字世界中存储、共享和/或分析。然而，这些内容的质量因印刷、存储和扫描过程在物理世界中引起的各种失真而严重下降。尽管从扫描副本中恢复高质量内容已成为许多产品不可或缺的任务，但这一问题尚未得到系统探索，且据我们所知，尚无公开数据集可用。在本文中，我们将此问题定义为“去扫描化”，并引入一个高质量大规模数据集DESCAN-18K。该数据集包含野外采集的18K对原始图像与扫描图像，其中包含多种复杂退化。为消除此类复杂退化，我们提出一种名为DescanDiffusion的新型图像恢复模型，该模型由一个纠正全局色彩退化的色彩编码器和一个去除局部退化的条件去噪扩散概率模型（DDPM）组成。为进一步提升DescanDiffusion的泛化能力，我们还设计了一种通过重现扫描图像中显著退化的合成数据生成方案。通过综合实验与分析，我们证明DescanDiffusion在客观和主观上均优于包括商业恢复产品在内的其他基线模型。