  We propose to restore old photos that suffer from severe degradation through
a deep learning approach. Unlike conventional restoration tasks that can be
solved through supervised learning, the degradation in real photos is complex
and the domain gap between synthetic images and real old photos makes the
network fail to generalize. Therefore, we propose a novel triplet domain
translation network by leveraging real photos along with massive synthetic
image pairs. Specifically, we train two variational autoencoders (VAEs) to
respectively transform old photos and clean photos into two latent spaces. And
the translation between these two latent spaces is learned with synthetic
paired data. This translation generalizes well to real photos because the
domain gap is closed in the compact latent space. Besides, to address multiple
degradations mixed in one old photo, we design a global branch with apartial
nonlocal block targeting to the structured defects, such as scratches and dust
spots, and a local branch targeting to the unstructured defects, such as noises
and blurriness. Two branches are fused in the latent space, leading to improved
capability to restore old photos from multiple defects. Furthermore, we apply
another face refinement network to recover fine details of faces in the old
photos, thus ultimately generating photos with enhanced perceptual quality.
With comprehensive experiments, the proposed pipeline demonstrates superior
performance over state-of-the-art methods as well as existing commercial tools
in terms of visual quality for old photos restoration.
