🤖 AI Summary
This paper presents a systematic survey of deep learning–based facial image restoration, focusing on denoising, super-resolution, deblurring, and artifact removal. Addressing challenges such as strong facial structural priors and complex degradation modeling, we propose the first holistic taxonomy of methods, a unified evaluation framework, and an open-source benchmark repository encompassing 20+ state-of-the-art approaches—including fully reproducible implementations. Leveraging datasets like CelebA and FFHQ, we conduct comprehensive cross-method evaluations using PSNR, SSIM, and LPIPS metrics, integrating CNN/Transformer architectures, perceptual and adversarial losses, and multi-scale feature fusion strategies. Our empirical analysis reveals performance boundaries and task-specific suitability across methods. Key contributions include: (1) the first structured, principle-driven classification system for facial restoration; (2) the first open, end-to-end benchmark platform supporting full method reproduction; (3) a rigorous, large-scale empirical study; and (4) concrete research directions concerning network design, evaluation paradigms, and dataset construction.
📝 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.
