A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal

• URL: http://arxiv.org/abs/2211.02831v1
• Date: Sat, 5 Nov 2022 07:08:15 GMT
• Title: A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal
• Authors: Tao Wang, Kaihao Zhang, Xuanxi Chen, Wenhan Luo, Jiankang Deng, Tong Lu, Xiaochun Cao, Wei Liu, Hongdong Li, Stefanos Zafeiriou
• Abstract summary: Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images. This paper comprehensively surveys recent advances in deep learning techniques for face restoration.
• Score: 177.21001709272144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 statistic 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 study deep learning-based face restoration methods systematically. Thus, this paper comprehensively surveys recent advances in deep learning techniques for face restoration. Specifically, we first summarize different problem formulations and analyze the characteristic of the face image. Second, we discuss the challenges of face restoration. Concerning these challenges, we present a comprehensive review of existing FR methods, including prior based methods and deep learning-based 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 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.

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