• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于优化变分模型的遥感图像去条带处理

Destriping of Remote Sensing Images by an Optimized Variational Model.

作者信息

Yan Fei, Wu Siyuan, Zhang Qiong, Liu Yunqing, Sun Haonan

机构信息

School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Jilin Provincial Science and Technology Innovation Center of Intelligent Perception and Information Processing, Changchun 130022, China.

出版信息

Sensors (Basel). 2023 Aug 30;23(17):7529. doi: 10.3390/s23177529.

DOI:10.3390/s23177529
PMID:37687987
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10490704/
Abstract

Satellite sensors often capture remote sensing images that contain various types of stripe noise. The presence of these stripes significantly reduces the quality of the remote images and severely affects their subsequent applications in other fields. Despite the existence of many stripe noise removal methods in the research, they often result in the loss of fine details during the destriping process, and some methods even generate artifacts. In this paper, we proposed a new unidirectional variational model to remove horizontal stripe noise. The proposed model fully considered the directional characteristics and structural sparsity of the stripe noise, as well as the prior features of the underlying image, to design different sparse constraints, and the ℓp quasinorm was introduced in these constraints to better describe these sparse characteristics, thus achieving a more excellent destriping effect. Moreover, we employed the fast alternating direction method of multipliers (ADMM) to solve the proposed non-convex model. This significantly improved the efficiency and robustness of the proposed method. The qualitative and quantitative results from simulated and real data experiments confirm that our method outperforms existing destriping approaches in terms of stripe noise removal and preservation of image details.

摘要

卫星传感器经常捕捉到包含各种条纹噪声的遥感图像。这些条纹的存在显著降低了遥感图像的质量,并严重影响其在其他领域的后续应用。尽管在研究中存在许多条纹噪声去除方法,但它们在去条纹过程中往往会导致精细细节的丢失,并且一些方法甚至会产生伪影。在本文中,我们提出了一种新的单向变分模型来去除水平条纹噪声。所提出的模型充分考虑了条纹噪声的方向特性和结构稀疏性,以及基础图像的先验特征,以设计不同的稀疏约束,并在这些约束中引入了ℓp拟范数来更好地描述这些稀疏特征,从而实现了更优异的去条纹效果。此外,我们采用快速交替方向乘子法(ADMM)来求解所提出的非凸模型。这显著提高了所提方法的效率和鲁棒性。模拟和真实数据实验的定性和定量结果证实,我们的方法在去除条纹噪声和保留图像细节方面优于现有的去条纹方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/8123aba5e745/sensors-23-07529-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/311252b50b4a/sensors-23-07529-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/59b9b1695df9/sensors-23-07529-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/3f8be5e13a13/sensors-23-07529-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/65cbaae5570b/sensors-23-07529-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/ea1d0ffac7e4/sensors-23-07529-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/1af9ff7092f7/sensors-23-07529-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/83a609eeb6d0/sensors-23-07529-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/2789166c42c6/sensors-23-07529-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/5547acbd4611/sensors-23-07529-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/c5b24f9bf35d/sensors-23-07529-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/9f2314b4c03f/sensors-23-07529-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/4759d6e441ad/sensors-23-07529-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/c68911dbf933/sensors-23-07529-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/9cffd280e279/sensors-23-07529-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/ca29e136a437/sensors-23-07529-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/e24ae7b9324d/sensors-23-07529-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/81181860c633/sensors-23-07529-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/0e56569fdefd/sensors-23-07529-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/ba43f7a18a25/sensors-23-07529-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/6bb9c5d82388/sensors-23-07529-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/bf88dd7c60ca/sensors-23-07529-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/69dc5d31085e/sensors-23-07529-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/8123aba5e745/sensors-23-07529-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/311252b50b4a/sensors-23-07529-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/59b9b1695df9/sensors-23-07529-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/3f8be5e13a13/sensors-23-07529-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/65cbaae5570b/sensors-23-07529-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/ea1d0ffac7e4/sensors-23-07529-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/1af9ff7092f7/sensors-23-07529-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/83a609eeb6d0/sensors-23-07529-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/2789166c42c6/sensors-23-07529-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/5547acbd4611/sensors-23-07529-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/c5b24f9bf35d/sensors-23-07529-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/9f2314b4c03f/sensors-23-07529-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/4759d6e441ad/sensors-23-07529-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/c68911dbf933/sensors-23-07529-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/9cffd280e279/sensors-23-07529-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/ca29e136a437/sensors-23-07529-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/e24ae7b9324d/sensors-23-07529-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/81181860c633/sensors-23-07529-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/0e56569fdefd/sensors-23-07529-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/ba43f7a18a25/sensors-23-07529-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/6bb9c5d82388/sensors-23-07529-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/bf88dd7c60ca/sensors-23-07529-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/69dc5d31085e/sensors-23-07529-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2cda/10490704/8123aba5e745/sensors-23-07529-g023.jpg

相似文献

1
Destriping of Remote Sensing Images by an Optimized Variational Model.基于优化变分模型的遥感图像去条带处理
Sensors (Basel). 2023 Aug 30;23(17):7529. doi: 10.3390/s23177529.
2
Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping.多光谱遥感图像去条纹的各向异性谱空全变差模型。
IEEE Trans Image Process. 2015 Jun;24(6):1852-66. doi: 10.1109/TIP.2015.2404782. Epub 2015 Feb 18.
3
An Innovative Approach for Removing Stripe Noise in Infrared Images.一种去除红外图像条纹噪声的创新方法。
Sensors (Basel). 2023 Jul 29;23(15):6786. doi: 10.3390/s23156786.
4
Robust destriping method with unidirectional total variation and framelet regularization.具有单向全变差和小框架正则化的稳健去条带方法。
Opt Express. 2013 Oct 7;21(20):23307-23. doi: 10.1364/OE.21.023307.
5
A Novel Stripe Noise Removal Model for Infrared Images.一种新型的红外图像条纹噪声去除模型。
Sensors (Basel). 2022 Apr 13;22(8):2971. doi: 10.3390/s22082971.
6
Stripe noise removal in conductive atomic force microscopy.导电原子力显微镜中的条纹噪声去除
Sci Rep. 2024 Feb 16;14(1):3931. doi: 10.1038/s41598-024-54094-w.
7
A Simplified Convex Optimization Model for Image Restoration with Multiplicative Noise.一种用于乘性噪声图像恢复的简化凸优化模型。
J Imaging. 2023 Oct 20;9(10):229. doi: 10.3390/jimaging9100229.
8
A Second-Order Method for Removing Mixed Noise from Remote Sensing Images.一种用于去除遥感图像中混合噪声的二阶方法。
Sensors (Basel). 2023 Aug 30;23(17):7543. doi: 10.3390/s23177543.
9
An Infrared Stripe Noise Removal Method Based on Multi-Scale Wavelet Transform and Multinomial Sparse Representation.一种基于多尺度小波变换和多项式稀疏表示的红外条纹噪声去除方法
Comput Intell Neurosci. 2022 May 30;2022:4044071. doi: 10.1155/2022/4044071. eCollection 2022.
10
Remote Sensing Image of The Landsat 8-9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function.基于拉普拉斯函数的非局部低秩正则化实现陆地卫星8-9号压缩感知的遥感图像
Entropy (Basel). 2023 Mar 17;25(3):523. doi: 10.3390/e25030523.

引用本文的文献

1
Stripe Noise Removal in Blazed Grating Generation for Electrically Tunable Beam Deflector.用于电调光束偏转器的闪耀光栅生成中的条纹噪声去除
Materials (Basel). 2025 Jan 10;18(2):291. doi: 10.3390/ma18020291.

本文引用的文献

1
Single-frame-based column fixed-pattern noise correction in an uncooled infrared imaging system based on weighted least squares.基于加权最小二乘法的非制冷红外成像系统中基于单帧的列固定模式噪声校正
Appl Opt. 2019 Nov 20;58(33):9141-9153. doi: 10.1364/AO.58.009141.
2
Hyperspectral Image Restoration Using Weighted Group Sparsity-Regularized Low-Rank Tensor Decomposition.基于加权组稀疏正则化低秩张量分解的高光谱图像复原
IEEE Trans Cybern. 2020 Aug;50(8):3556-3570. doi: 10.1109/TCYB.2019.2936042. Epub 2019 Sep 2.
3
Stripe and ring artifact removal with combined wavelet--Fourier filtering.
结合小波--傅里叶滤波去除条纹和环形伪影
Opt Express. 2009 May 11;17(10):8567-91. doi: 10.1364/oe.17.008567.
4
Image quality assessment: from error visibility to structural similarity.图像质量评估:从误差可见性到结构相似性。
IEEE Trans Image Process. 2004 Apr;13(4):600-12. doi: 10.1109/tip.2003.819861.