• 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

基于晶格 Boltzmann 方法的图像去噪。

A Lattice Boltzmann method for image denoising.

机构信息

Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

出版信息

IEEE Trans Image Process. 2009 Dec;18(12):2797-802. doi: 10.1109/TIP.2009.2028369. Epub 2009 Jul 24.

DOI:10.1109/TIP.2009.2028369
PMID:19635695
Abstract

In this paper, we construct a Lattice Boltzmann scheme to simulate the well known total variation based restoration model, that is, ROF model. The advantages of the Lattice Boltzmann method include the fast computational speed and the easily implemented fully parallel algorithm. A conservative property of the LB method is discussed. The macroscopic PDE associated with the LB algorithm is derived which is just the ROF model. Moreover, the linearized stability of the method is analyzed. The numerical computations demonstrate that the LB algorithm is efficient and robust. Even though the quality of the restored images is slightly lower than those by using the ROF model, the restored images of the LB method are satisfactory. Furthermore, computational speed of the LB method is much faster than ROF model. In general, CPU time of the LB method for restored images is about one tenth of ROF model.

摘要

在本文中,我们构建了一个格子玻尔兹曼方案来模拟著名的全变分重建模型,即 ROF 模型。格子玻尔兹曼方法的优点包括计算速度快和易于实现的完全并行算法。讨论了 LB 方法的保守性质。推导出与 LB 算法相关的宏观 PDE,它恰好是 ROF 模型。此外,还分析了方法的线性稳定性。数值计算表明,LB 算法是高效和稳健的。尽管重建图像的质量略低于使用 ROF 模型的质量,但 LB 方法的重建图像令人满意。此外,LB 方法的计算速度比 ROF 模型快得多。一般来说,LB 方法用于恢复图像的 CPU 时间约为 ROF 模型的十分之一。

相似文献

1
A Lattice Boltzmann method for image denoising.基于晶格 Boltzmann 方法的图像去噪。
IEEE Trans Image Process. 2009 Dec;18(12):2797-802. doi: 10.1109/TIP.2009.2028369. Epub 2009 Jul 24.
2
Combining cellular automata and Lattice Boltzmann method to model multiscale avascular tumor growth coupled with nutrient diffusion and immune competition.将元胞自动机和格子玻尔兹曼方法相结合,对多尺度无血管肿瘤生长进行建模,同时考虑营养物质扩散和免疫竞争。
J Immunol Methods. 2012 Feb 28;376(1-2):55-68. doi: 10.1016/j.jim.2011.11.009. Epub 2011 Dec 2.
3
A lattice Boltzmann algorithm for electro-osmotic flows in microfluidic devices.一种用于微流控装置中电渗流的格子玻尔兹曼算法。
J Chem Phys. 2005 Apr 8;122(14):144907. doi: 10.1063/1.1874813.
4
Preconditioned lattice-Boltzmann method for steady flows.用于稳态流动的预处理格子玻尔兹曼方法。
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Dec;70(6 Pt 2):066706. doi: 10.1103/PhysRevE.70.066706. Epub 2004 Dec 21.
5
Application of the split-gradient method to 3D image deconvolution in fluorescence microscopy.分裂梯度法在荧光显微镜三维图像去卷积中的应用。
J Microsc. 2009 Apr;234(1):47-61. doi: 10.1111/j.1365-2818.2009.03150.x.
6
Implicit and explicit solvent models for the simulation of a single polymer chain in solution: Lattice Boltzmann versus Brownian dynamics.用于模拟溶液中单链聚合物的隐式和显式溶剂模型:格子玻尔兹曼与布朗动力学。
J Chem Phys. 2009 Oct 28;131(16):164114. doi: 10.1063/1.3251771.
7
A nonlinear total variation-based denoising method with two regularization parameters.一种具有两个正则化参数的基于非线性全变分的去噪方法。
IEEE Trans Biomed Eng. 2009 Mar;56(3):582-6. doi: 10.1109/TBME.2008.2011561. Epub 2009 Jan 23.
8
Combining molecular dynamics with Lattice Boltzmann: a hybrid method for the simulation of (charged) colloidal systems.结合分子动力学与格子玻尔兹曼方法:一种用于模拟(带电)胶体系统的混合方法。
J Chem Phys. 2005 May 8;122(18):184903. doi: 10.1063/1.1890905.
9
Simulation of nasal flow by lattice Boltzmann methods.基于格子玻尔兹曼方法的鼻腔气流模拟。
Comput Biol Med. 2007 Jun;37(6):739-49. doi: 10.1016/j.compbiomed.2006.06.013. Epub 2006 Sep 7.
10
Image denoising using mixtures of projected Gaussian Scale Mixtures.使用投影高斯尺度混合模型的图像去噪
IEEE Trans Image Process. 2009 Aug;18(8):1689-702. doi: 10.1109/TIP.2009.2022006. Epub 2009 May 2.