A Weak Target Enhancement Method in the Bearing Time Recording

Jianfeng Tao
{"title":"A Weak Target Enhancement Method in the Bearing Time Recording","authors":"Jianfeng Tao","doi":"10.1145/3316551.3316552","DOIUrl":null,"url":null,"abstract":"A method which combines a new image detection operator with one dimensional subsection histogram equalization(NIDO-ODSHE) is proposed to deal with the problem that the target cannot be better detected on the time recording due to the influence of environmental noise, disturbance and so on. Firstly, slide detections of a target trajectory is carried out based on beam data using the new image detection operator. Then the detected data is processed by the adaptive subsection histogram equalization, and the new time recording is finally obtained. After such a treatment, the processed image gets better gain, the image clarity is obviously improved, and the weak target trajectory is significantly enhanced. Moreover, the NIDO-ODSHE method does not need parameter setting and the computational amount is small. Simulation and experimental results on a time recording of and comparisons with the histogram equalization show that the NIDO-ODSHE method can enhance the weak target on a time recording and improve the artificial/automatic detection ability of weak targets, the proposed method obviously improves the image, and increases the mean squared error ratio and the image clarity more than 12% and 6%, respectively.","PeriodicalId":300199,"journal":{"name":"Proceedings of the 2019 3rd International Conference on Digital Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 3rd International Conference on Digital Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316551.3316552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

A method which combines a new image detection operator with one dimensional subsection histogram equalization(NIDO-ODSHE) is proposed to deal with the problem that the target cannot be better detected on the time recording due to the influence of environmental noise, disturbance and so on. Firstly, slide detections of a target trajectory is carried out based on beam data using the new image detection operator. Then the detected data is processed by the adaptive subsection histogram equalization, and the new time recording is finally obtained. After such a treatment, the processed image gets better gain, the image clarity is obviously improved, and the weak target trajectory is significantly enhanced. Moreover, the NIDO-ODSHE method does not need parameter setting and the computational amount is small. Simulation and experimental results on a time recording of and comparisons with the histogram equalization show that the NIDO-ODSHE method can enhance the weak target on a time recording and improve the artificial/automatic detection ability of weak targets, the proposed method obviously improves the image, and increases the mean squared error ratio and the image clarity more than 12% and 6%, respectively.
方位时间记录中的弱目标增强方法
提出了一种新的图像检测算子与一维分段直方图均衡化(NIDO-ODSHE)相结合的方法,以解决在时间记录中由于环境噪声、干扰等影响而无法较好地检测目标的问题。首先,利用新的图像检测算子对光束数据进行目标轨迹的滑动检测;然后对检测到的数据进行自适应分段直方图均衡化处理,最终得到新的时间记录。经过这样的处理,处理后的图像获得了较好的增益,图像清晰度明显提高,弱目标轨迹明显增强。此外,NIDO-ODSHE方法不需要设置参数,计算量小。实时记录的仿真和实验结果以及与直方图均衡化方法的对比表明,NIDO-ODSHE方法可以增强实时记录的弱目标,提高弱目标的人工/自动检测能力,所提方法对图像有明显改善,均方误差比和图像清晰度分别提高了12%和6%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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