  Physical photographs now can be conveniently scanned by smartphones and
stored forever as a digital version, yet the scanned photos are not restored
well. One solution is to train a supervised deep neural network on many digital
photos and the corresponding scanned photos. However, it requires a high labor
cost, leading to limited training data. Previous works create training pairs by
simulating degradation using image processing techniques. Their synthetic
images are formed with perfectly scanned photos in latent space. Even so, the
real-world degradation in smartphone photo scanning remains unsolved since it
is more complicated due to lens defocus, lighting conditions, losing details
via printing. Besides, locally structural misalignment still occurs in data due
to distorted shapes captured in a 3-D world, reducing restoration performance
and the reliability of the quantitative evaluation. To solve these problems, we
propose a semi-supervised Deep Photo Scan (DPScan). First, we present a way of
producing real-world degradation and provide the DIV2K-SCAN dataset for
smartphone-scanned photo restoration. Also, Local Alignment is proposed to
reduce the minor misalignment remaining in data. Second, we simulate many
different variants of the real-world degradation using low-level image
transformation to gain a generalization in smartphone-scanned image properties,
then train a degradation network to generalize all styles of degradation and
provide pseudo-scanned photos for unscanned images as if they were scanned by a
smartphone. Finally, we propose a Semi-Supervised Learning that allows our
restoration network to be trained on both scanned and unscanned images,
diversifying training image content. As a result, the proposed DPScan
quantitatively and qualitatively outperforms its baseline architecture,
state-of-the-art academic research, and industrial products in smartphone photo
scanning.
