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

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.Comment: 21 pages, 19 figure

Weighted non-locally self-similarity sparse representation for face deblurring

Abstract The human face is one of the most interesting subjects in various computer vision tasks. In recent years, significant progress has been made for generic image deblurring problem, but existing popular sparse representation based deblurring methods are not able to achieve excellent results on blurry face images. The failure of these methods mainly stems from the lack of local/non-local self-similarity prior knowledge. There are many similar non-local patches in the neighborhood of a given patch in a face image, therefore, this property should be effectively exploited to obtain a good estimation of the sparse coding coefficients. In this paper, we introduce the current weighted non-locally self-similarity (WNLSS) method [1], which is originally proposed to remove the noise for natural images, into the face deblurring model. There are two terms in the WNLSS sparse representation model, data fidelity term and regularization term. Based on the theoretical analysis, we show the properties of data fidelity term and regularization term also can fit well for face deblurring problem. The results also demonstrate that WNLSS method can achieve excellent performance in terms of both synthetic and real blurred face dataset