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.

面部修复是图像修复中的一个专业领域，旨在将低质量的面部图像恢复为高质量的面部图像。本论文首先考察了导致真实世界低质量图像的普遍因素，并介绍了合成低质量图像的降质技术。我们还讨论了该领域常用的重要基准，并根据不同任务对面部修复方法进行分类并解释了它们的演化过程。此外，我们探讨了在修复过程中常用的各种面部先验知识，并讨论了增强其有效性的策略。在实验部分，我们使用统一基准全面评估了最先进的面部修复方法在各种任务上的性能，并从不同的角度分析了它们的表现。最后，我们讨论了面部修复领域面临的挑战，并提出了未来发展的潜在方向。本工作的开源代码库可以在 https://github.com/24wenjie-li/Awesome-Face-Restoration 找到。