Zhiqiang Wang, Zhuangzhi Yan, Y. Qian, George Chen
{"title":"Lattice Boltzmann model of anisotropic diffusion for image denoising","authors":"Zhiqiang Wang, Zhuangzhi Yan, Y. Qian, George Chen","doi":"10.1109/YCICT.2010.5713142","DOIUrl":null,"url":null,"abstract":"To overcome the inefficiency of the traditional numerical methods that implement the anisotropic diffusion model for image denoising, a novel lattice Boltzmann model of anisotropic diffusion is presented in this paper. In the model, the diffusion rate is adapted to the image itself and independently set for each direction of diffusion. The mass accumulation is calculated through a weighted summation of the particle distribution functions. Our method is stable with large iteration steps thus reducing the iteration steps greatly. The experiment results showed that compared to others, our approach performs better in terms of the resulting images as well as computing efficiency. In addition, our approach is easy for parallel implementation.","PeriodicalId":179847,"journal":{"name":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YCICT.2010.5713142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
To overcome the inefficiency of the traditional numerical methods that implement the anisotropic diffusion model for image denoising, a novel lattice Boltzmann model of anisotropic diffusion is presented in this paper. In the model, the diffusion rate is adapted to the image itself and independently set for each direction of diffusion. The mass accumulation is calculated through a weighted summation of the particle distribution functions. Our method is stable with large iteration steps thus reducing the iteration steps greatly. The experiment results showed that compared to others, our approach performs better in terms of the resulting images as well as computing efficiency. In addition, our approach is easy for parallel implementation.