Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistical priors and degradation models, which are difficult to meet the requirements of real-world applications in practice. In recent years, face restoration has witnessed great progress after stepping into the deep learning era. However, there are few works to systematically study the deep learning based face restoration methods. Thus, in this paper, we provide a comprehensive survey of recent advances in deep learning techniques for face restoration. Specifically, we first summarize different problem formulations and analyze the characteristics of face images. Second, we discuss the challenges of face restoration. With regard to these challenges, we present a comprehensive review of recent FR methods, including prior-based methods and deep-learning methods. Then, we explore developed techniques in the task of FR covering network architectures, loss functions, and benchmark datasets. We also conduct a systematic benchmark evaluation on representative methods. Finally, we discuss the future directions including network designs, metrics, benchmark datasets, applications, etc. We also provide an open source repository for all the discussed methods, which is available at https://github.com/TaoWangzj/Awesome-Face-Restoration.

翻译：人脸复原旨在从低质量输入图像中恢复高质量人脸，是低层计算机视觉领域中的一项特定图像复原问题。早期人脸复原方法主要采用统计先验与退化模型，难以满足实际应用场景的需求。近年来，随着深度学习时代的到来，人脸复原取得了显著进展。然而，目前鲜有工作系统性地研究基于深度学习的人脸复原方法。为此，本文对深度学习技术在人脸复原领域的最新进展进行了全面综述。具体而言，我们首先归纳了不同的问题建模方式，并分析了人脸图像的特性；其次，讨论了人脸复原面临的挑战；针对这些挑战，我们对近期的人脸复原方法进行了综合评述，涵盖基于先验的方法与深度学习方法；随后，探究了人脸复原任务中涉及的技术发展，包括网络架构、损失函数和基准数据集；同时，我们对代表性方法进行了系统性的基准评估；最后，讨论了未来研究方向，包括网络设计、评估指标、基准数据集、应用等。我们还为所有讨论的方法提供了开源资源库，地址为 https://github.com/TaoWangzj/Awesome-Face-Restoration。