基于深度学习的面部修复技术综述

Survey of facial completion techniques based on deep learning

传统图像修复算法在修复区域涉及复杂非重复结构(如面部)时,不能准确捕捉到高级语义。近三年来基于深度学习的方法被应用于图像修复中,其修复结果的结构相似性较传统方法提高了10%以上。首先阐述了面部修复技术的研究发展历程,主要介绍了基于深度学习的面部修复算法,将其分为无监督和有监督两大类方法,在每一类中重点对近年来涌现的各种面部修复算法进行分析和总结;然后归纳了当前主流的六类图像数据集,以及算法性能评价指标;最后讨论了面部修复技术的未来研究方向。

Traditional image inpainting algorithms cannot accurately capture high-level semantics when regions involve complex non-repetitive structures,such as faces.In the past three years,the method based on deep learning has been applied to image inpainting,and the structural similarity of the repair results had increased by more than 10%compared with the traditional methods.This paper firstly expounded the research and development process of facial completion technologies,mainly introduced the face repair algorithm based on deep learning,which was divided into unsupervised and supervised categories.In each category,it focused on the various facial completion algorithm ideas that had emerged in recent years.Then it summarized the six mainstream types of image datasets.Two evaluation indexes were summarized in this paper.Finally,it discussed the future research direction of facial completion technique.