基于可变空间指数偏微分方程的图像去噪

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED Applied and Computational Harmonic Analysis Pub Date : 2023-11-10 DOI:10.1016/j.acha.2023.101608
Amine Laghrib, Lekbir Afraites
{"title":"基于可变空间指数偏微分方程的图像去噪","authors":"Amine Laghrib,&nbsp;Lekbir Afraites","doi":"10.1016/j.acha.2023.101608","DOIUrl":null,"url":null,"abstract":"<div><p>Image denoising is always considered an important area of image processing. In this work, we address a new PDE-based model for image denoising that have been contaminated by multiplicative noise<span>, specially the Speckle one. We propose a new class of PDEs whose nonlinear structure depends on a spatially tensor depending quantity attached to the desired solution, which takes into account the gray level information by introducing a gray level indicator function in the diffusion coefficient<span>. We give some theoretical results, discretization and also stability condition for the suggested model. Finally, we carry out some numerical results to approve the effectiveness of our model by comparing the results obtained with some competitive models.</span></span></p></div>","PeriodicalId":55504,"journal":{"name":"Applied and Computational Harmonic Analysis","volume":"68 ","pages":"Article 101608"},"PeriodicalIF":2.6000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image denoising based on a variable spatially exponent PDE\",\"authors\":\"Amine Laghrib,&nbsp;Lekbir Afraites\",\"doi\":\"10.1016/j.acha.2023.101608\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Image denoising is always considered an important area of image processing. In this work, we address a new PDE-based model for image denoising that have been contaminated by multiplicative noise<span>, specially the Speckle one. We propose a new class of PDEs whose nonlinear structure depends on a spatially tensor depending quantity attached to the desired solution, which takes into account the gray level information by introducing a gray level indicator function in the diffusion coefficient<span>. We give some theoretical results, discretization and also stability condition for the suggested model. Finally, we carry out some numerical results to approve the effectiveness of our model by comparing the results obtained with some competitive models.</span></span></p></div>\",\"PeriodicalId\":55504,\"journal\":{\"name\":\"Applied and Computational Harmonic Analysis\",\"volume\":\"68 \",\"pages\":\"Article 101608\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied and Computational Harmonic Analysis\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1063520323000957\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied and Computational Harmonic Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1063520323000957","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0

摘要

图像去噪一直被认为是图像处理的一个重要领域。在这项工作中,我们提出了一种新的基于pde的图像去噪模型,用于被乘性噪声污染的图像去噪,特别是斑点噪声。我们提出了一类新的偏微分方程,它的非线性结构依赖于附加在期望解上的空间张量,它通过在扩散系数中引入灰度指示函数来考虑灰度信息。给出了模型的一些理论结果、离散化和稳定性条件。最后,通过与一些竞争模型的比较,进行了数值计算,验证了模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Image denoising based on a variable spatially exponent PDE

Image denoising is always considered an important area of image processing. In this work, we address a new PDE-based model for image denoising that have been contaminated by multiplicative noise, specially the Speckle one. We propose a new class of PDEs whose nonlinear structure depends on a spatially tensor depending quantity attached to the desired solution, which takes into account the gray level information by introducing a gray level indicator function in the diffusion coefficient. We give some theoretical results, discretization and also stability condition for the suggested model. Finally, we carry out some numerical results to approve the effectiveness of our model by comparing the results obtained with some competitive models.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied and Computational Harmonic Analysis
Applied and Computational Harmonic Analysis 物理-物理:数学物理
CiteScore
5.40
自引率
4.00%
发文量
67
审稿时长
22.9 weeks
期刊介绍: Applied and Computational Harmonic Analysis (ACHA) is an interdisciplinary journal that publishes high-quality papers in all areas of mathematical sciences related to the applied and computational aspects of harmonic analysis, with special emphasis on innovative theoretical development, methods, and algorithms, for information processing, manipulation, understanding, and so forth. The objectives of the journal are to chronicle the important publications in the rapidly growing field of data representation and analysis, to stimulate research in relevant interdisciplinary areas, and to provide a common link among mathematical, physical, and life scientists, as well as engineers.
期刊最新文献
On quadrature for singular integral operators with complex symmetric quadratic forms Gaussian approximation for the moving averaged modulus wavelet transform and its variants Naimark-spatial families of equichordal tight fusion frames Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning Unlimited sampling beyond modulo
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1