  To improve the performance of blind face restoration, recent works mainly
treat the two aspects, i.e., generic and specific restoration, separately. In
particular, generic restoration attempts to restore the results through general
facial structure prior, while on the one hand, cannot generalize to real-world
degraded observations due to the limited capability of direct CNNs' mappings in
learning blind restoration, and on the other hand, fails to exploit the
identity-specific details. On the contrary, specific restoration aims to
incorporate the identity features from the reference of the same identity, in
which the requirement of proper reference severely limits the application
scenarios. Generally, it is a challenging and intractable task to improve the
photo-realistic performance of blind restoration and adaptively handle the
generic and specific restoration scenarios with a single unified model. Instead
of implicitly learning the mapping from a low-quality image to its high-quality
counterpart, this paper suggests a DMDNet by explicitly memorizing the generic
and specific features through dual dictionaries. First, the generic dictionary
learns the general facial priors from high-quality images of any identity,
while the specific dictionary stores the identity-belonging features for each
person individually. Second, to handle the degraded input with or without
specific reference, dictionary transform module is suggested to read the
relevant details from the dual dictionaries which are subsequently fused into
the input features. Finally, multi-scale dictionaries are leveraged to benefit
the coarse-to-fine restoration. Moreover, a new high-quality dataset, termed
CelebRef-HQ, is constructed to promote the exploration of specific face
restoration in the high-resolution space.
