  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.
