  Face restoration (FR) is a specialized field within image restoration that
aims to recover low-quality (LQ) face images into high-quality (HQ) face
images. Recent advances in deep learning technology have led to significant
progress in FR methods. In this paper, we begin by examining the prevalent
factors responsible for real-world LQ images and introduce degradation
techniques used to synthesize LQ images. We also discuss notable benchmarks
commonly utilized in the field. Next, we categorize FR methods based on
different tasks and explain their evolution over time. Furthermore, we explore
the various facial priors commonly utilized in the restoration process and
discuss strategies to enhance their effectiveness. In the experimental section,
we thoroughly evaluate the performance of state-of-the-art FR methods across
various tasks using a unified benchmark. We analyze their performance from
different perspectives. Finally, we discuss the challenges faced in the field
of FR and propose potential directions for future advancements. The open-source
repository corresponding to this work can be found at https:// github.com/
24wenjie-li/ Awesome-Face-Restoration.
