A Novel Exemplar-Based Image Inpainting Algorithm

Y. Liu, Chanjuan Liu, Hailin Zou, Shusen Zhou, Qian Shen, Tongtong Chen
{"title":"A Novel Exemplar-Based Image Inpainting Algorithm","authors":"Y. Liu, Chanjuan Liu, Hailin Zou, Shusen Zhou, Qian Shen, Tongtong Chen","doi":"10.1109/INCoS.2015.15","DOIUrl":null,"url":null,"abstract":"A novel exemplar-based image inpainting algorithm is proposed for solving the deficiencies of the classical Criminisi method, such as the error repair accumulation, high time complexity caused by the unreasonable design of the patch priority, inaccuracy criterion and its global search strategy. Thus, we construct a local structure measurement function by introducing the structure tensor theory, and optimize the means of patch priority. On that basis, we design a matching criterion. Experiments show that the improved algorithm has greater advantages on the fidelity of image structure compared with the Criminisi method. Besides, the improved algorithm makes progresses in both subjective visual effect and objective indexes, such as peak signal-to-noise ratio (PSNR), repair error and the running time compared with some of the typical image restoration algorithms proposed recent years.","PeriodicalId":345650,"journal":{"name":"2015 International Conference on Intelligent Networking and Collaborative Systems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Intelligent Networking and Collaborative Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INCoS.2015.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

A novel exemplar-based image inpainting algorithm is proposed for solving the deficiencies of the classical Criminisi method, such as the error repair accumulation, high time complexity caused by the unreasonable design of the patch priority, inaccuracy criterion and its global search strategy. Thus, we construct a local structure measurement function by introducing the structure tensor theory, and optimize the means of patch priority. On that basis, we design a matching criterion. Experiments show that the improved algorithm has greater advantages on the fidelity of image structure compared with the Criminisi method. Besides, the improved algorithm makes progresses in both subjective visual effect and objective indexes, such as peak signal-to-noise ratio (PSNR), repair error and the running time compared with some of the typical image restoration algorithms proposed recent years.
一种新的基于样本的图像绘制算法
针对经典Criminisi方法存在的错误修复累积、补丁优先级设计不合理导致的时间复杂度高、精度准则不准确及其全局搜索策略等不足,提出了一种基于样本的图像修复算法。为此,我们引入结构张量理论,构造了局部结构测量函数,并对patch优先级方法进行了优化。在此基础上,设计了匹配准则。实验表明,改进后的算法在图像结构保真度上比犯罪法有更大的优势。此外,与近年来提出的一些典型图像恢复算法相比,改进算法在主观视觉效果和峰值信噪比(PSNR)、修复误差和运行时间等客观指标上都取得了进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信