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<html><head><title>Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion - EUDL</title><link rel="icon" href="/images/favicon.ico"><link rel="stylesheet" type="text/css" href="/css/screen.css"><link rel="stylesheet" href="/css/zenburn.css"><meta http-equiv="Content-Type" content="charset=utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="Description" content="At present, visible light imaging sensor and infrared imaging sensor are two commonly used sensors, which are widely used in aviation, navigation and other military fields of detection, monitoring and tracking. Due to their different working principles, their performance is di"><meta name="citation_title" content="Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion"><meta name="citation_publication_date" content="2021/11/22"><meta name="citation_online_date" content="2021/11/22"><meta name="citation_author" content="Jihong Wang"><meta name="citation_author" content="Haiyan Yu"><meta name="citation_pdf_url" content="http://eudl.eu/pdf/10.4108/eai.22-11-2021.172216"><meta name="citation_journal_title" content="EAI Endorsed Transactions on Scalable Information Systems"><meta name="citation_issn" content="2032-9407"><meta name="citation_volume" content=""9""><meta name="citation_issue" content="36"><script type="text/javascript" src="https://services.eai.eu//load-signup-form/EAI"></script><script type="text/javascript" src="https://services.eai.eu//ujs/forms/signup/sso-client.js"></script><script type="text/javascript">if 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href="http://eai.eu/">EAI</a></nav></section></header><div id="eaientran"></div><section id="content"><section id="article"><section class="cover-and-filters"><a href="https://eai.eu/eai-sponsorship/?mtm_campaign=call%20for%20bids&mtm_kwd=bids&mtm_source=organize%20conference%20page&mtm_medium=eudl"><img src="https://eudl.eu/images/banner-outside.png"></a></section><section class="info-and-search"><span class="article-journal-ref"><a href="/journal/sis">sis<span class="info-separator"> </span><strong>22</strong><span class="info-separator">(</span>36<span class="info-separator">)</span><span class="info-separator">: </span>e6</a></span><p class="article-type">Research Article</p><h1>Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion</h1><section id="download"><a class="download-pdf" href="/pdf/10.4108/eai.22-11-2021.172216" title="Download eai.22-11-2021.172216.pdf">Download</a><span class="download">1321 downloads</span></section><section class="meta-section cite-area"><dl class="main-metadata"><dt class="title" id="cite-switchers">Cite</dt> <dd class="value" id="cite-switchers"><a href="" class="bibtex">BibTeX</a> <a href="" class="plain-text">Plain Text</a></dd></dl></section><ul id="cite-blocks"><li class="bibtex"><pre>@ARTICLE{10.4108/eai.22-11-2021.172216, author={Jihong Wang and Haiyan Yu}, title={Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={36}, publisher={EAI}, journal_a={SIS}, year={2021}, month={11}, keywords={infrared and visible image fusion, double-channel cascade, generative adversarial network, power equipment}, doi={10.4108/eai.22-11-2021.172216} } </pre></li><li class="plain-text"><div class="cite">Jihong Wang<br>Haiyan Yu<br>Year: 2021<br>Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion<br>SIS<br>EAI<br>DOI: 10.4108/eai.22-11-2021.172216</div></li></ul><section id="authors"><span class="author-list">Jihong Wang<sup>1</sup>, Haiyan Yu<sup>1</sup><sup>,*</sup></span><ul class="affiliation-list"><li>1: School of Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450000 China</li></ul><section class="corresponding-email">*Contact email: zkdywjh@126.com</section></section><section class="full-abstract"><h2>Abstract</h2><div><p>At present, visible light imaging sensor and infrared imaging sensor are two commonly used sensors, which are widely used in aviation, navigation and other military fields of detection, monitoring and tracking. Due to their different working principles, their performance is different. The infrared imaging sensor records the infrared radiation information of the target itself by acquiring the infrared radiation of the ground target. It identifies the target by detecting the thermal radiation difference between the target and the background, so it has special recognition and camouflage ability, such as finding people, vehicles and artillery hidden in the woods and grass. Although the infrared imaging sensor has a good detection performance for thermal targets, it is insensitive to the brightness changes of the scene and has low imaging resolution, which is not conducive to human eyes interpretation. Visible light imaging sensor is sensitive to the reflection of the target scene and has nothing to do with the thermal contrast of the target scene. The obtained image has high clarity and can provide the details of the target scene. Therefore, the fusion of infrared and visible images will be beneficial to the combination of infrared image's better target indication characteristics and visible image's scene clearing information. In this paper, we propose a double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion. The experimental results show that the fusion image not only retains the target information of the infrared image, but also retains more details of the visible image, and achieves better performance in both subjective and objective evaluation</p></div></section><section class="metas-section"><section class="meta-section"><dl class="main-metadata"><dt class="title">Keywords</dt> <dd class="value">infrared and visible image fusion, double-channel cascade, generative adversarial network, power equipment</dd></dl></section><section class="meta-section"><dl class="main-metadata"><dt class="title">Received</dt> <dd class="value">2021-11-11</dd><dt class="title">Accepted</dt> <dd class="value">2021-11-20</dd><dt class="title">Published</dt> <dd class="value">2021-11-22</dd><dt class="title">Publisher</dt> <dd class="value">EAI</dd></dl></section><section class="meta-section"><dl class="main-metadata"><dt class="title"></dt> <dd class="value"><a href="http://dx.doi.org/10.4108/eai.22-11-2021.172216">http://dx.doi.org/10.4108/eai.22-11-2021.172216</a></dd></dl></section></section><div class="copyright-block"><p>Copyright 漏 2021 Jihong Wang et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.</p></div></section></section></section><div class="clear"></div><footer><div class="links"><a href="https://www.ebsco.com/" target="_blank"><img class="logo ebsco-logo" src="/images/ebsco.png" alt="EBSCO"></a><a href="https://www.proquest.com/" target="_blank"><img class="logo proquest-logo" src="/images/proquest.png" alt="ProQuest"></a><a href="https://dblp.uni-trier.de/db/journals/publ/icst.html" target="_blank"><img class="logo dblp-logo" src="/images/dblp.png" alt="DBLP"></a><a href="https://doaj.org/search?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3A%7B%22index.publisher.exact%22%3A%22European%20Alliance%20for%20Innovation%20(EAI)%22%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22european%20alliance%20for%20innovation%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22index.publisher%22%7D%7D%7D%7D%7Dj" target="_blank"><img class="logo doaj-logo" src="/images/doaj.jpg" alt="DOAJ"></a><a href="https://www.portico.org/publishers/eai/" target="_blank"><img class="logo portico-logo" src="/images/portico.png" alt="Portico"></a><a href="http://eai.eu/" target="_blank"><img class="logo eai-logo" src="/images/eai.png"></a></div></footer></div><div class="footer-container"><div class="footer-width"><div class="footer-column logo-column"><a href="https://eai.eu/"><img src="https://eudl.eu/images/logo_new-1-1.png" alt="EAI Logo"></a></div><div class="footer-column"><h4>About EAI</h4><ul><li><a href="https://eai.eu/who-we-are/">Who We Are</a></li><li><a href="https://eai.eu/leadership/">Leadership</a></li><li><a href="https://eai.eu/research-areas/">Research Areas</a></li><li><a href="https://eai.eu/partners/">Partners</a></li><li><a href="https://eai.eu/media-center/">Media Center</a></li></ul></div><div class="footer-column"><h4>Community</h4><ul><li><a href="https://eai.eu/eai-community/">Membership</a></li><li><a href="https://eai.eu/conferences/">Conference</a></li><li><a href="https://eai.eu/recognition/">Recognition</a></li><li><a href="https://eai.eu/corporate-sponsorship">Sponsor Us</a></li></ul></div><div class="footer-column"><h4>Publish with EAI</h4><ul><li><a href="https://eai.eu/publishing">Publishing</a></li><li><a href="https://eai.eu/journals/">Journals</a></li><li><a href="https://eai.eu/proceedings/">Proceedings</a></li><li><a href="https://eai.eu/books/">Books</a></li><li><a href="https://eudl.eu/">EUDL</a></li></ul></div></div></div><script type="text/javascript" src="https://eudl.eu/js/gacode.js"></script><script src="/js/highlight.pack.js"></script><script>hljs.initHighlightingOnLoad();</script><script type="application/ld+json">{"@context":"http://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"item":{"@id":"http://eudl.eu","name":"Home","image":null}},{"@type":"ListItem","position":2,"item":{"@id":"http://eudl.eu/journals","name":"Journals","image":null}},{"@type":"ListItem","position":3,"item":{"@id":"http://eudl.eu/journal/sis","name":"sis","image":null}},{"@type":"ListItem","position":4,"item":{"@id":"/issue/sis/9/36","name":"Issue 36","image":null}},{"@type":"ListItem","position":1,"item":{"@id":"http://eudl.eu/doi/10.4108/eai.22-11-2021.172216","name":"Double-channel cascade-based generative adversarial network for power equipment infrared and visible image fusion","image":null}}]}</script></body></html>