武汉工程大学学报2024，Vol.46Issue(5)：564-573,10.DOI:10.19843/j.cnki.CN42-1779/TQ.202211016
实际场景人脸超分辨率算法综述
Review of real-world face super-resolution algorithms
摘要
Abstract
Face super-resolution enhances resolution and quality of low-resolution facial images,finding wide applications in fields of video surveillance,criminal investigation,and entertainment.However,in real-world scenarios,imperfect imaging systems,recording equipment,transmission media,and processing methods resulted in irregular combinations of degradation processes that reduce image quality,like noise and blurring.Training network models based on explicit degradation processes alone cannot meet practical needs.This paper reviews the principles of non-blind and blind face super-resolution techniques,the commonly used datasets and evaluation metrics and the subjective and objective reconstruction results of representative works in the field of face super-resolution.Future related researches should focus on multi-modal information decision fusion and tensor fusion to improve the feature dimension and temporal similarity of the reconstructed images;enhance the generalization ability of the model through large-scale pre-training and adversarial learning;investigate the impact of identity consistency algorithms and technologies such as transfer learning on complex imaging conditions.关键词
人脸超分辨率/实际场景/深度学习/降质过程Key words
face super-resolution/real-world/deep learning/degradation process分类
信息技术与安全科学引用本文复制引用
朱柏霖,卢涛,王依伊,饶茜雅,赵康辉,张彦铎..实际场景人脸超分辨率算法综述[J].武汉工程大学学报,2024,46(5):564-573,10.基金项目
国家自然科学基金(62072350,62171328) （62072350,62171328）
