A Hybrid Multi-focus Image Fusion Technique using SWT and PCA

Tushar Tyagi, Parth Gupta, Prabhishek Singh
{"title":"A Hybrid Multi-focus Image Fusion Technique using SWT and PCA","authors":"Tushar Tyagi, Parth Gupta, Prabhishek Singh","doi":"10.1109/Confluence47617.2020.9057960","DOIUrl":null,"url":null,"abstract":"This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. It is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal Image Quality Index), and PSNR (Peak Signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).","PeriodicalId":180005,"journal":{"name":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Confluence47617.2020.9057960","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. It is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal Image Quality Index), and PSNR (Peak Signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).
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
基于SWT和PCA的混合多焦点图像融合技术
针对多聚焦图像,提出了一种新的混合并行处理图像融合技术。这里使用了两种不同的方法,即平稳小波变换(SWT)和主成分分析(PCA),它们并行地对输入图像进行处理。这两种方法应用于相同的输入数据集。该方法虽然在计算速度上比所比较的方法慢一些,但仍然显示出更好的结果。将SWT和PCA得到的融合后的图像再用PCA方法进行融合。这是一种并行处理技术。并将该方法与DWT、SWT、PCA等传统方法和常规方法进行了比较。结果表明,所提方法的计算结果优于对比方法。采用CC(相关系数)、UIQI(通用图像质量指数)和PSNR(峰值信噪比)对所提方法的结果进行定性(视觉外观)和定量分析。该技术可用于视觉传感器网络(VSN)的实时应用。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Identification of the most efficient algorithm to find Hamiltonian Path in practical conditions Segmentation and Detection of Road Region in Aerial Images using Hybrid CNN-Random Field Algorithm A Novel Approach for Isolation of Sinkhole Attack in Wireless Sensor Networks Performance Analysis of various Information Platforms for recognizing the quality of Indian Roads Time Series Data Analysis And Prediction Of CO2 Emissions
×
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