Review of Pixel-Level Image Fusion

Image fusion can be performed at different levels： signal, pixel, feature and symbol levels. Almost all image fusion algorithms developed to date fall into pixel level. This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative...

Full description

Saved in:
Bibliographic Details
Published in:Shanghai jiao tong da xue xue bao 2010-02, Vol.15 (1), p.6-12
Main Author: 杨波 敬忠良 赵海涛
Format: Article
Language:English
Subjects:
Architecture
Computer Science
Electrical Engineering
Engineering
Life Sciences
Materials Science
像素水平
像素级
图像融合算法
多尺度
相对优势
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c262y-705ce135796c9cbb9b65335dc3e9282d2b1f7dce01f4e77b714ebe0d259593a23
cites cdi_FETCH-LOGICAL-c262y-705ce135796c9cbb9b65335dc3e9282d2b1f7dce01f4e77b714ebe0d259593a23
container_end_page 12
container_issue 1
container_start_page 6
container_title Shanghai jiao tong da xue xue bao
container_volume 15
creator 杨波 敬忠良 赵海涛
description Image fusion can be performed at different levels： signal, pixel, feature and symbol levels. Almost all image fusion algorithms developed to date fall into pixel level. This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative strengths and weaknesses. Particular emphasis is placed on multiscale-based methods. Some performance measures practicable for pixel-level image fusion are also discussed. At last, prospects of pixel-level image fusion are made.
doi_str_mv 10.1007/s12204-010-7186-y
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_743740764</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cqvip_id>32928366</cqvip_id><sourcerecordid>743740764</sourcerecordid><originalsourceid>FETCH-LOGICAL-c262y-705ce135796c9cbb9b65335dc3e9282d2b1f7dce01f4e77b714ebe0d259593a23</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EEqXwAeyiblgZPH7GS1TxqFQJhGBtJc6kpKRJG5NC_h5X6ZrVzOKcGd1LyDWwW2DM3AXgnEnKgFEDqabDCZmAtYqmkKancY8QBTD8nFyEsGZMMiHshMzecF_hT9KWyWv1izVd4h7rZLHJVpg89qFqm0tyVmZ1wKvjnJKPx4f3-TNdvjwt5vdL6rnmAzVMeQShjNXe-jy3uVZCqMILtDzlBc-hNIVHBqVEY3IDEnNkBVdWWZFxMSU3491t1-56DN9uUwWPdZ012PbBGSmMZEbLSMJI-q4NocPSbbtqk3WDA-YOSd1Yh4t1uEMdbogOH50Q2WaFnVu3fdfEQP9Ks-Ojz7ZZ7aLn8sx_lVWNTvAYS2gt_gCcYGxb</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>743740764</pqid></control><display><type>article</type><title>Review of Pixel-Level Image Fusion</title><source>Springer Nature</source><creator>杨波 敬忠良 赵海涛</creator><creatorcontrib>杨波 敬忠良 赵海涛</creatorcontrib><description>Image fusion can be performed at different levels： signal, pixel, feature and symbol levels. Almost all image fusion algorithms developed to date fall into pixel level. This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative strengths and weaknesses. Particular emphasis is placed on multiscale-based methods. Some performance measures practicable for pixel-level image fusion are also discussed. At last, prospects of pixel-level image fusion are made.</description><identifier>ISSN: 1007-1172</identifier><identifier>EISSN: 1995-8188</identifier><identifier>DOI: 10.1007/s12204-010-7186-y</identifier><language>eng</language><publisher>Heidelberg: Shanghai Jiaotong University Press</publisher><subject>Architecture ; Computer Science ; Electrical Engineering ; Engineering ; Life Sciences ; Materials Science ; 像素水平 ; 像素级 ; 图像融合算法 ; 多尺度 ; 相对优势</subject><ispartof>Shanghai jiao tong da xue xue bao, 2010-02, Vol.15 (1), p.6-12</ispartof><rights>Shanghai Jiaotong University and Springer Berlin Heidelberg 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c262y-705ce135796c9cbb9b65335dc3e9282d2b1f7dce01f4e77b714ebe0d259593a23</citedby><cites>FETCH-LOGICAL-c262y-705ce135796c9cbb9b65335dc3e9282d2b1f7dce01f4e77b714ebe0d259593a23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/85391X/85391X.jpg</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>杨波 敬忠良 赵海涛</creatorcontrib><title>Review of Pixel-Level Image Fusion</title><title>Shanghai jiao tong da xue xue bao</title><addtitle>J. Shanghai Jiaotong Univ. (Sci.)</addtitle><addtitle>Journal of Shanghai Jiaotong university</addtitle><description>Image fusion can be performed at different levels： signal, pixel, feature and symbol levels. Almost all image fusion algorithms developed to date fall into pixel level. This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative strengths and weaknesses. Particular emphasis is placed on multiscale-based methods. Some performance measures practicable for pixel-level image fusion are also discussed. At last, prospects of pixel-level image fusion are made.</description><subject>Architecture</subject><subject>Computer Science</subject><subject>Electrical Engineering</subject><subject>Engineering</subject><subject>Life Sciences</subject><subject>Materials Science</subject><subject>像素水平</subject><subject>像素级</subject><subject>图像融合算法</subject><subject>多尺度</subject><subject>相对优势</subject><issn>1007-1172</issn><issn>1995-8188</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRS0EEqXwAeyiblgZPH7GS1TxqFQJhGBtJc6kpKRJG5NC_h5X6ZrVzOKcGd1LyDWwW2DM3AXgnEnKgFEDqabDCZmAtYqmkKancY8QBTD8nFyEsGZMMiHshMzecF_hT9KWyWv1izVd4h7rZLHJVpg89qFqm0tyVmZ1wKvjnJKPx4f3-TNdvjwt5vdL6rnmAzVMeQShjNXe-jy3uVZCqMILtDzlBc-hNIVHBqVEY3IDEnNkBVdWWZFxMSU3491t1-56DN9uUwWPdZ012PbBGSmMZEbLSMJI-q4NocPSbbtqk3WDA-YOSd1Yh4t1uEMdbogOH50Q2WaFnVu3fdfEQP9Ks-Ojz7ZZ7aLn8sx_lVWNTvAYS2gt_gCcYGxb</recordid><startdate>201002</startdate><enddate>201002</enddate><creator>杨波 敬忠良 赵海涛</creator><general>Shanghai Jiaotong University Press</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201002</creationdate><title>Review of Pixel-Level Image Fusion</title><author>杨波 敬忠良 赵海涛</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c262y-705ce135796c9cbb9b65335dc3e9282d2b1f7dce01f4e77b714ebe0d259593a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Architecture</topic><topic>Computer Science</topic><topic>Electrical Engineering</topic><topic>Engineering</topic><topic>Life Sciences</topic><topic>Materials Science</topic><topic>像素水平</topic><topic>像素级</topic><topic>图像融合算法</topic><topic>多尺度</topic><topic>相对优势</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>杨波 敬忠良 赵海涛</creatorcontrib><collection>中文科技期刊数据库</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>中文科技期刊数据库-7.0平台</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Shanghai jiao tong da xue xue bao</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>杨波 敬忠良 赵海涛</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Review of Pixel-Level Image Fusion</atitle><jtitle>Shanghai jiao tong da xue xue bao</jtitle><stitle>J. Shanghai Jiaotong Univ. (Sci.)</stitle><addtitle>Journal of Shanghai Jiaotong university</addtitle><date>2010-02</date><risdate>2010</risdate><volume>15</volume><issue>1</issue><spage>6</spage><epage>12</epage><pages>6-12</pages><issn>1007-1172</issn><eissn>1995-8188</eissn><abstract>Image fusion can be performed at different levels： signal, pixel, feature and symbol levels. Almost all image fusion algorithms developed to date fall into pixel level. This paper provides an overview of the most widely used pixel-level image fusion algorithms and some comments about their relative strengths and weaknesses. Particular emphasis is placed on multiscale-based methods. Some performance measures practicable for pixel-level image fusion are also discussed. At last, prospects of pixel-level image fusion are made.</abstract><cop>Heidelberg</cop><pub>Shanghai Jiaotong University Press</pub><doi>10.1007/s12204-010-7186-y</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1007-1172
ispartof Shanghai jiao tong da xue xue bao, 2010-02, Vol.15 (1), p.6-12
issn 1007-1172
1995-8188
language eng
recordid cdi_proquest_miscellaneous_743740764
source Springer Nature
subjects Architecture
Computer Science
Electrical Engineering
Engineering
Life Sciences
Materials Science
像素水平
像素级
图像融合算法
多尺度
相对优势
title Review of Pixel-Level Image Fusion
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T20%3A50%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Review%20of%20Pixel-Level%20Image%20Fusion&rft.jtitle=Shanghai%20jiao%20tong%20da%20xue%20xue%20bao&rft.au=%E6%9D%A8%E6%B3%A2%20%E6%95%AC%E5%BF%A0%E8%89%AF%20%E8%B5%B5%E6%B5%B7%E6%B6%9B&rft.date=2010-02&rft.volume=15&rft.issue=1&rft.spage=6&rft.epage=12&rft.pages=6-12&rft.issn=1007-1172&rft.eissn=1995-8188&rft_id=info:doi/10.1007/s12204-010-7186-y&rft_dat=%3Cproquest_cross%3E743740764%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c262y-705ce135796c9cbb9b65335dc3e9282d2b1f7dce01f4e77b714ebe0d259593a23%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=743740764&rft_id=info:pmid/&rft_cqvip_id=32928366&rfr_iscdi=true