  Physical photographs now can be conveniently scanned by smartphones and
stored forever as a digital version, but 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, human annotation costs a
huge resource 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 real lens defocus, lighting conditions,
losing details via printing, various photo materials, and more. To solve these
problems, we propose a Deep Photo Scan (DPScan) based on semi-supervised
learning. First, we present the way to produce real-world degradation and
provide the DIV2K-SCAN dataset for smartphone-scanned photo restoration.
Second, by using DIV2K-SCAN, we adopt the concept of Generative Adversarial
Networks to learn how to degrade a high-quality image as if it were scanned by
a real smartphone, then generate pseudo-scanned photos for unscanned photos.
Finally, we propose to train on the scanned and pseudo-scanned photos
representing a semi-supervised approach with a cycle process as: high-quality
images --> real-/pseudo-scanned photos --> reconstructed images. The proposed
semi-supervised scheme can balance between supervised and unsupervised errors
while optimizing to limit imperfect pseudo inputs but still enhance
restoration. 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.
