SPN三阈值统计检测滤波器噪声检测程序的能力研究

{"title":"SPN三阈值统计检测滤波器噪声检测程序的能力研究","authors":"","doi":"10.1109/icce-asia46551.2019.8942190","DOIUrl":null,"url":null,"abstract":"This paper aims to investigate the TTSD (Triple Threshold Statistical Detection) filter because the TTSD filter, which has been proposed since 2018, is one of the highest recent powerful noise eliminating procedures because an auxiliary condition is first implemented in this procedure which satisfactorily solves the disadvantage of previous noise detection procedures. Moreover, the noise signature is estimated for all pixels and statistically checked with the first threshold to classify noisy pixels succeeds by the statistically checking of the computed pixel with the 2nd and 3rd threshold levels where these thresholds are based on both Gaussian statistical dispersion (mean and standard deviation) and quartile dispersion (median). In this simulation part, many contaminated images, which are comprised of Resolution, Girl, Lena, Pepper, Mobile and Pentagon under salt and pepper noise at many noise densities in the noisy detecting accuracy, noiseless detecting accuracy and overall detecting accuracy perspective.","PeriodicalId":117814,"journal":{"name":"2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Competence Investigation of Noise Detecting Procedure Found on TTSD (Triple Threshold Statistical Detection) filter for SPN\",\"authors\":\"\",\"doi\":\"10.1109/icce-asia46551.2019.8942190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to investigate the TTSD (Triple Threshold Statistical Detection) filter because the TTSD filter, which has been proposed since 2018, is one of the highest recent powerful noise eliminating procedures because an auxiliary condition is first implemented in this procedure which satisfactorily solves the disadvantage of previous noise detection procedures. Moreover, the noise signature is estimated for all pixels and statistically checked with the first threshold to classify noisy pixels succeeds by the statistically checking of the computed pixel with the 2nd and 3rd threshold levels where these thresholds are based on both Gaussian statistical dispersion (mean and standard deviation) and quartile dispersion (median). In this simulation part, many contaminated images, which are comprised of Resolution, Girl, Lena, Pepper, Mobile and Pentagon under salt and pepper noise at many noise densities in the noisy detecting accuracy, noiseless detecting accuracy and overall detecting accuracy perspective.\",\"PeriodicalId\":117814,\"journal\":{\"name\":\"2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)\",\"volume\":\"116 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/icce-asia46551.2019.8942190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icce-asia46551.2019.8942190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文旨在研究TTSD(三阈值统计检测)滤波器,因为自2018年以来提出的TTSD滤波器是最近最强大的噪声消除程序之一,因为该程序首先实现了一个辅助条件,令人满意地解决了以前噪声检测程序的缺点。此外,估计所有像素的噪声特征,并使用第一个阈值进行统计检查,通过使用第2和第3个阈值水平对计算的像素进行统计检查,从而成功地对噪声像素进行分类,其中这些阈值基于高斯统计离散度(平均值和标准差)和四分位数离散度(中位数)。在本仿真部分中,在有噪声检测精度、无噪声检测精度和整体检测精度的角度下,对盐噪声和胡椒噪声下的分辨率、Girl、Lena、Pepper、Mobile和Pentagon组成的多幅污染图像进行了仿真。
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
Competence Investigation of Noise Detecting Procedure Found on TTSD (Triple Threshold Statistical Detection) filter for SPN
This paper aims to investigate the TTSD (Triple Threshold Statistical Detection) filter because the TTSD filter, which has been proposed since 2018, is one of the highest recent powerful noise eliminating procedures because an auxiliary condition is first implemented in this procedure which satisfactorily solves the disadvantage of previous noise detection procedures. Moreover, the noise signature is estimated for all pixels and statistically checked with the first threshold to classify noisy pixels succeeds by the statistically checking of the computed pixel with the 2nd and 3rd threshold levels where these thresholds are based on both Gaussian statistical dispersion (mean and standard deviation) and quartile dispersion (median). In this simulation part, many contaminated images, which are comprised of Resolution, Girl, Lena, Pepper, Mobile and Pentagon under salt and pepper noise at many noise densities in the noisy detecting accuracy, noiseless detecting accuracy and overall detecting accuracy perspective.
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
期刊最新文献
Illumination Invariant Thermal Face Recognition using Convolutional Neural Network Fusion Technology of 3D Point Cloud Map for Objects Classification Tour Miner: Mining System of Tour Plans from SNS: Extraction of Travel Records from Check-in Information Portable Blood Typing Device Using Image Analysis Ambient Mode: A Novel Service and Intelligent Control based on User Awareness using BLE and Wi-Fi
×
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