Research Area:  Machine Learning

Abstract:

Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution face images, is a domain-specific image super-resolution problem. Recently, FSR has received considerable attention and witnessed dazzling advances with the development of deep learning techniques. To date, few summaries of the studies on the deep learning-based FSR are available. In this survey, we present a comprehensive review of deep learning-based FSR methods in a systematic manner. First, we summarize the problem formulation of FSR and introduce popular assessment metrics and loss functions. Second, we elaborate on the facial characteristics and popular datasets used in FSR. Third, we roughly categorize existing methods according to the utilization of facial characteristics. In each category, we start with a general description of design principles, present an overview of representative approaches, and then discuss the pros and cons among them. Fourth, we evaluate the performance of some state-of-the-art methods. Fifth, joint FSR and other tasks, and FSR-related applications are roughly introduced. Finally, we envision the prospects of further technological advancement in this field.

Keywords:  
Deep Learning
Face Super-resolution
low-resolution
Machine Learning

Author(s) Name:   Junjun Jiang , Chenyang Wang , Xianming Liu , Jiayi Ma

Publisher name:  ACM

DOI:  10.1145/3485132

Volume Information:  Volume 55,Issue 1,Article No.: 13,pp 1–36

Paper Link:   https://dl.acm.org/doi/full/10.1145/3485132