Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired outputs, or 2) complement high-quality details lost in the inputs. In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces.

2022: Shangchen Zhou, Kelvin C. K. Chan, Chongyi Li, Chen Change Loy

Ranked #1 on Blind Face Restoration on WIDER

https://arxiv.org/pdf/2206.11253v2.pdf