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

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.

Altered global signal topography in Alzheimer's disease

“…Face restoration is to restore the high-quality face image from the degraded face image [35]. Face restoration is divided Non-prior and Prior based methods.…”

Efficient Face Region Occlusion Repair Based on T-GANs

“…Zhang et al [31] proposed a DeBLuRring Network (DBLRNet) by applying 3D convolutions to perform spatio-temporal learning in both spatial and temporal domains for restoring blurred images in videos. Zhang et al [32] Designed a multi-facial prior search network (MFPSNet) to optimally extract information from different facial priors for blind face restoration (BFR) tasks [33,34]. Ge et al [35] proposed Identity Diversity GAN (ID-GAN), which integrates the face recognizer of CNN into GAN, uses CNN for feature reconstruction, and GAN for visual reconstruction, generating realistic and preserving identity feature images.…”

Benchmarking Ultra-High-Definition Image Super-resolution

“…Recently, natural language processing (NLP) model Transformer proposed by Vaswani et al has obtained superior performance against state-of-the-art methods in the computer vision community for various vision tasks. Transformer models have been successfully utilized for image recognition [51]- [54], object detection [55]- [60], image classification [53], [58], [61]- [64], image segmentation [58], [64]- [67] and face restoration [68], [69].…”