Deep Learning-based Face Super-resolution: A Survey
- URL: http://arxiv.org/abs/2101.03749v1
- Date: Mon, 11 Jan 2021 08:17:11 GMT
- Title: Deep Learning-based Face Super-resolution: A Survey
- Authors: Junjun Jiang, Chenyang Wang, Xianming Liu, and Jiayi Ma
- Abstract summary: Face super-resolution, also known as face hallucination, is a domain-specific image super-resolution problem.
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.
- Score: 78.11274281686246
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 each category, we start with a general description of design
principles, present an overview of representative approaches, and compare the
similarities and differences among various methods. Finally, we envision
prospects for further technical advancement in this field.
Related papers
- Super-Resolving Face Image by Facial Parsing Information [52.1267613768555]
Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one.
We build a novel parsing map guided face super-resolution network which extracts the face prior from low-resolution face image.
High-resolution features contain more precise spatial information while low-resolution features provide strong contextual information.
arXiv Detail & Related papers (2023-04-06T08:19:03Z) - Cross-resolution Face Recognition via Identity-Preserving Network and
Knowledge Distillation [12.090322373964124]
Cross-resolution face recognition is a challenging problem for modern deep face recognition systems.
This paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image.
arXiv Detail & Related papers (2023-03-15T14:52:46Z) - Guided Depth Map Super-resolution: A Survey [88.54731860957804]
Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image.
A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques.
This survey is an effort to present a comprehensive survey of recent progress in GDSR.
arXiv Detail & Related papers (2023-02-19T15:43:54Z) - A survey on facial image deblurring [3.6775758132528877]
When the facial image is blurred, it has a great impact on high-level vision tasks such as face recognition.
This paper surveys and summarizes recently published methods for facial image deblurring, most of which are based on deep learning.
We show the performance of classical methods on datasets and metrics and give a brief discussion on the differences of model-based and learning-based methods.
arXiv Detail & Related papers (2023-02-10T02:24:56Z) - A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur,
Artifact Removal [177.21001709272144]
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.
arXiv Detail & Related papers (2022-11-05T07:08:15Z) - Face Super-Resolution with Progressive Embedding of Multi-scale Face
Priors [4.649637261351803]
We propose a novel recurrent convolutional network based framework for face super-resolution.
We take full advantage of the intermediate outputs of the recurrent network, and landmarks information and facial action units (AUs) information are extracted.
Our proposed method significantly outperforms state-of-the-art FSR methods in terms of image quality and facial details restoration.
arXiv Detail & Related papers (2022-10-12T08:16:52Z) - LR-to-HR Face Hallucination with an Adversarial Progressive
Attribute-Induced Network [67.64536397027229]
Face super-resolution is a challenging and highly ill-posed problem.
We propose an end-to-end progressive learning framework incorporating facial attributes.
We show that the proposed approach can yield satisfactory face hallucination images outperforming other state-of-the-art approaches.
arXiv Detail & Related papers (2021-09-29T19:50:45Z) - Analysis and evaluation of Deep Learning based Super-Resolution
algorithms to improve performance in Low-Resolution Face Recognition [0.0]
Super-resolution algorithms may be able to recover the discriminant properties of the subjects involved.
This project aimed at evaluating and adapting different deep neural network architectures for the task of face super-resolution.
Experiments showed that general super-resolution architectures might enhance face verification performance of deep neural networks trained on high-resolution faces.
arXiv Detail & Related papers (2021-01-19T02:41:57Z) - Face Super-Resolution Guided by 3D Facial Priors [92.23902886737832]
We propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures.
Our work is the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes.
The proposed 3D priors achieve superior face super-resolution results over the state-of-the-arts.
arXiv Detail & Related papers (2020-07-18T15:26:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.