Review of Image Inpainting Methods

被引：1
|
作者：
Li Xuetao [1 ]
Wang Yaoxiong [2 ]
Gao Fang [1 ]
机构：
[1] Guangxi Univ, Sch Elect Engn, Nanning 530004, Guangxi, Peoples R China
[2] Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
来源：
LASER & OPTOELECTRONICS PROGRESS | 2023年 / 60卷 / 02期
关键词：
image inpainting; deep learning; convolutional neural network; auto encoder network; generative adversarial network; NETWORK; SCENE;
D O I：
10.3788/LOP212680
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Image inpainting is a hot topic in the field of computer vision. It is a process that enables filling in damaged regions with alternative contents by estimating the relevant information either from surrounding areas or external data. With the advent of big data, image inpainting methods based on deep learning have attracted significant attention in image processing because of their excellent performance. This paper presents a brief review of existing image inpainting approaches and discusses the network structure and performance of each algorithm, along with a comparison of widely used datasets. In view of the existing challenges in this field, this paper proposes potential research directions and developmental trends in image inpainting.
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页数：16
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