一种基于非均匀三角分割和插值的图像去噪算法

K. U, Nian Ji, Dongxu Qi, Zesheng Tang, Ruixia Song
{"title":"一种基于非均匀三角分割和插值的图像去噪算法","authors":"K. U, Nian Ji, Dongxu Qi, Zesheng Tang, Ruixia Song","doi":"10.1109/ICFPEE.2010.23","DOIUrl":null,"url":null,"abstract":"Distinguishing from the traditional denoising methods, this paper proposes a novel denoising algorithm according to the image-surface fitting after the Non-Uniform Triangular Partition. A given image can automatically be partitioned into different triangles with different dimensions and the bivariate polynomial is used to do the Optimal Quadratic Approximation to gray values of image in each sub-triangle. When the approximation error and bivariate polynomial are specified, a specific image partition result is obtained. The partitioning codes obtained can be used to reconstruct the original image. In general, the smallest the error, the better approximation effect is obtained. However, we should select a suitable error to get the best approximation to original image instead of the noised image. On the other hand, in order to avoid the triangle effect after denoising and obtain a better denoising result, the interpolation method is used before and after the denoising by Non-Uniform Triangular Partition. Experimental results show that this method can obtain a better denoising effect by comparing with other methods to some extend.","PeriodicalId":412111,"journal":{"name":"2010 International Conference on Future Power and Energy Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A Novel Image Denoising Algorithm Based on Non-Uniform Triangular Partition and Interpolation\",\"authors\":\"K. U, Nian Ji, Dongxu Qi, Zesheng Tang, Ruixia Song\",\"doi\":\"10.1109/ICFPEE.2010.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Distinguishing from the traditional denoising methods, this paper proposes a novel denoising algorithm according to the image-surface fitting after the Non-Uniform Triangular Partition. A given image can automatically be partitioned into different triangles with different dimensions and the bivariate polynomial is used to do the Optimal Quadratic Approximation to gray values of image in each sub-triangle. When the approximation error and bivariate polynomial are specified, a specific image partition result is obtained. The partitioning codes obtained can be used to reconstruct the original image. In general, the smallest the error, the better approximation effect is obtained. However, we should select a suitable error to get the best approximation to original image instead of the noised image. On the other hand, in order to avoid the triangle effect after denoising and obtain a better denoising result, the interpolation method is used before and after the denoising by Non-Uniform Triangular Partition. Experimental results show that this method can obtain a better denoising effect by comparing with other methods to some extend.\",\"PeriodicalId\":412111,\"journal\":{\"name\":\"2010 International Conference on Future Power and Energy Engineering\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Future Power and Energy Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICFPEE.2010.23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Future Power and Energy Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFPEE.2010.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

与传统的去噪方法不同,本文提出了一种基于非均匀三角分割后图像表面拟合的去噪算法。将给定图像自动分割成不同维数的三角形,利用二元多项式对各子三角形的灰度值进行二次逼近。当确定近似误差和二元多项式时,得到特定的图像分割结果。得到的分割码可用于重建原始图像。一般情况下,误差越小,逼近效果越好。然而,我们应该选择一个合适的误差,以获得与原始图像的最佳逼近,而不是噪声图像。另一方面,为了避免去噪后的三角效应,获得更好的去噪效果,在非均匀三角分割去噪前后采用插值方法。实验结果表明,与其他方法相比,该方法能在一定程度上获得更好的去噪效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Image Denoising Algorithm Based on Non-Uniform Triangular Partition and Interpolation
Distinguishing from the traditional denoising methods, this paper proposes a novel denoising algorithm according to the image-surface fitting after the Non-Uniform Triangular Partition. A given image can automatically be partitioned into different triangles with different dimensions and the bivariate polynomial is used to do the Optimal Quadratic Approximation to gray values of image in each sub-triangle. When the approximation error and bivariate polynomial are specified, a specific image partition result is obtained. The partitioning codes obtained can be used to reconstruct the original image. In general, the smallest the error, the better approximation effect is obtained. However, we should select a suitable error to get the best approximation to original image instead of the noised image. On the other hand, in order to avoid the triangle effect after denoising and obtain a better denoising result, the interpolation method is used before and after the denoising by Non-Uniform Triangular Partition. Experimental results show that this method can obtain a better denoising effect by comparing with other methods to some extend.
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
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