A new image fusion algorithm based on second generation wavelet transform

Bin Zhang, Yongguo Zheng, Weiqi Fang, Li-min Cui
{"title":"A new image fusion algorithm based on second generation wavelet transform","authors":"Bin Zhang, Yongguo Zheng, Weiqi Fang, Li-min Cui","doi":"10.1109/CINC.2010.5643811","DOIUrl":null,"url":null,"abstract":"A novel image fusion algorithm based on wavelet lifting scheme is presented. The lifting scheme is a new idea of constructing wavelets and has several unique advantages over conventional convolution-based wavelet transform. It allows for an in-place implementation of wavelet transform and reduces computation time and memory requirement greatly. So in this fusion algorithm, the lifting wavelet transform is employed to decompose and reconstruct images to realize fast image fusion. Meanwhile, a new local feature-based fusion rule is put forward to improve fusion quality and extract all significant features from multi-source images. Extensive experiments on the fusion of registered SPOT Panchromatic/multi-spectral images, multi-focus digital camera images,and medical MRI/CT images have been performed. Finally, visual judgment and several objective evaluation criterions are taken as the means of assessing fusion performance. The experimental results show that the new approach is very effective and is able to fuse multi-source images.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643811","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

A novel image fusion algorithm based on wavelet lifting scheme is presented. The lifting scheme is a new idea of constructing wavelets and has several unique advantages over conventional convolution-based wavelet transform. It allows for an in-place implementation of wavelet transform and reduces computation time and memory requirement greatly. So in this fusion algorithm, the lifting wavelet transform is employed to decompose and reconstruct images to realize fast image fusion. Meanwhile, a new local feature-based fusion rule is put forward to improve fusion quality and extract all significant features from multi-source images. Extensive experiments on the fusion of registered SPOT Panchromatic/multi-spectral images, multi-focus digital camera images,and medical MRI/CT images have been performed. Finally, visual judgment and several objective evaluation criterions are taken as the means of assessing fusion performance. The experimental results show that the new approach is very effective and is able to fuse multi-source images.
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
基于第二代小波变换的图像融合新算法
提出了一种基于小波提升方案的图像融合算法。提升方案是一种构造小波的新思路,与传统的基于卷积的小波变换相比,具有许多独特的优点。它允许小波变换的就地实现,大大减少了计算时间和内存需求。因此,在该融合算法中,采用提升小波变换对图像进行分解和重构,实现图像的快速融合。同时,提出了一种新的基于局部特征的融合规则,以提高融合质量,并从多源图像中提取所有重要特征。对配准的SPOT全色/多光谱图像、多焦数码相机图像和医学MRI/CT图像进行了大量的融合实验。最后以视觉判断和若干客观评价标准作为评价融合性能的手段。实验结果表明,该方法能够有效地融合多源图像。
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
Evolutionary design of ANN structure using genetic algorithm Performance analysis of spread spectrum communication system in fading enviornment and Interference Comprehensive evaluation of forest industries based on rough sets and artificial neural network A new descent algorithm with curve search rule for unconstrained minimization A multi-agent simulation for intelligence economy
×
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