Towards Authentic Face Restoration with Iterative Diffusion Models and
  Beyond
Yang Zhao Tingbo Hou Yu-Chuan Su Xuhui Jia. Yandong Li Matthias Grundmann
An authentic face restoration system is becoming increasingly demanding in
many computer vision applications, e.g., image enhancement, video
communication, and taking portrait. Most of the advanced face restoration
models can recover high-quality faces from low-quality ones but usually fail to
faithfully generate realistic and high-frequency details that are favored by
users. To achieve authentic restoration, we propose $\textbf{IDM}$, an
$\textbf{I}$teratively learned face restoration system based on denoising
$\textbf{D}$iffusion $\textbf{M}$odels (DDMs). We define the criterion of an
authentic face restoration system, and argue that denoising diffusion models
are naturally endowed with this property from two aspects: intrinsic iterative
refinement and extrinsic iterative enhancement. Intrinsic learning can preserve
the content well and gradually refine the high-quality details, while extrinsic
enhancement helps clean the data and improve the restoration task one step
further. We demonstrate superior performance on blind face restoration tasks.
Beyond restoration, we find the authentically cleaned data by the proposed
restoration system is also helpful to image generation tasks in terms of
training stabilization and sample quality. Without modifying the models, we
achieve better quality than state-of-the-art on FFHQ and ImageNet generation
using either GANs or diffusion models.