Face Hallucination Techniques: A Survey

Rajput, Shyam Singh; Arya, K. V.; Singh, Vinay; Bohat, Vijay Kumar

doi:10.1109/infocomtech.2018.8722416

Cited by 14 publications

(7 citation statements)

References 71 publications

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“…[34] pays more attention to reviewing deep learning methods for general image restoration tasks that include image deblurring, denoising, dehazing, and super-resolution. (2) The second group [35], [36], [37], [38] focuses on reviewing the advances and development in traditional face super-resolution methods such as subspace learning based methods [14], [15], and sparse representation based methods [16], [17]. (3) The third group [39], [40] reviews the recent development in face superresolution with deep learning techniques.…”

Section: Differences From Other Related Reviewsmentioning

confidence: 99%

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

Wang¹,

Chen²,

Luo³

et al. 2022

Preprint

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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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Section: Differences From Other Related Reviewsmentioning

confidence: 99%

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

Wang¹,

Chen²,

Luo³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…the addition of image features not actually present, which may or may not be useful. [76][77][78][79]…”

Section: Limitations Of Gansmentioning

confidence: 99%

Deepfakes in Ophthalmology

Chen

Coyner

Chan

et al. 2021

Ophthalmology Science

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“…Face super resolution: a survey [73] 2017 IJIGSP 5 Super-resolution for biometrics: a comprehensive survey [122] 2018 PR 6 Face hallucination techniques: a survey [128] 2018 CICT 7 Survey on GAN-based face hallucination with its model development [103] 2019 IET GAN-based methods.…”

Section: Facementioning

confidence: 99%

“…Towards face super-resolution, a domain-specific image super-resolution, a few surveys are listed in Table 1. In the early stage of research, [98,150,5,73,122,128] provide a comprehensive review of traditional face super-resolution methods (mainly including patch-based super-resolution, PCA-based methods, etc. ), while Liu et al [103] offers a generative adversarial network (GAN) based face super-resolution model.…”

Section: Facementioning

confidence: 99%

Deep Learning-based Face Super-Resolution: A Survey

Jiang¹,

Wang²,

Liu³

et al. 2021

Preprint

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Face super-resolution, also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) one or a sequence of face images to generate the corresponding high-resolution (HR) face images, is a domain-specific image super-resolution problem. Recently, face super-resolution has received considerable attention, and witnessed dazzling advances with deep learning techniques. To date, few summaries of the studies on the deep learning-based face super-resolution are available. In this survey, we present a comprehensive review of deep learning techniques in face super-resolution in a systematic manner. First, we summarize the problem formulation of face super-resolution. Second, we compare the differences between generic image super-resolution and face super-resolution. Third, datasets and performance metrics commonly used in facial hallucination are presented. Fourth, we roughly categorize existing methods according to the utilization of face-specific information. In

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Face Hallucination Techniques: A Survey

Cited by 14 publications

References 71 publications

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

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

Deepfakes in Ophthalmology

Deep Learning-based Face Super-Resolution: A Survey

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