{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:41:48Z","timestamp":1760233308840,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan University and Yibin Municipal People\u2019s Government University and City strategic cooperation special fund project","award":["2020CDYB-29","2021ZYSF007","2020YFS0575","2021KJT0012-2021YFS0067"],"award-info":[{"award-number":["2020CDYB-29","2021ZYSF007","2020YFS0575","2021KJT0012-2021YFS0067"]}]},{"name":"Science and Technology plan transfer payment project of Sichuan province","award":["2020CDYB-29","2021ZYSF007","2020YFS0575","2021KJT0012-2021YFS0067"],"award-info":[{"award-number":["2020CDYB-29","2021ZYSF007","2020YFS0575","2021KJT0012-2021YFS0067"]}]},{"name":"The Key Research and Development Program of Science and Technology Department of Sichuan Province","award":["2020CDYB-29","2021ZYSF007","2020YFS0575","2021KJT0012-2021YFS0067"],"award-info":[{"award-number":["2020CDYB-29","2021ZYSF007","2020YFS0575","2021KJT0012-2021YFS0067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In remote sensing, the fusion of infrared and visible images is one of the common means of data processing. Its aim is to synthesize one fused image with abundant common and differential information from the source images. At present, the fusion methods based on deep learning are widely employed in this work. However, the existing fusion network with deep learning fails to effectively integrate common and differential information for source images. To alleviate the problem, we propose a dual-head fusion strategy and contextual information awareness fusion network (DCFusion) to preserve more meaningful information from source images. Firstly, we extract multi-scale features for the source images with multiple convolution and pooling layers. Then, we propose a dual-headed fusion strategy (DHFS) to fuse different modal features from the encoder. The DHFS can effectively preserve common and differential information for different modal features. Finally, we propose a contextual information awareness module (CIAM) to reconstruct the fused image. The CIAM can adequately exchange information from different scale features and improve fusion performance. Furthermore, the whole network was tested on MSRS and TNO datasets. The results of extensive experiments prove that our proposed network achieves good performance in target maintenance and texture preservation for fusion images.<\/jats:p>","DOI":"10.3390\/rs15010144","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:30:27Z","timestamp":1672205427000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DCFusion: Dual-Headed Fusion Strategy and Contextual Information Awareness for Infrared and Visible Remote Sensing Image"],"prefix":"10.3390","volume":"15","author":[{"given":"Qin","family":"Pu","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-6062","authenticated-orcid":false,"given":"Abdellah","family":"Chehri","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0651-4278","authenticated-orcid":false,"given":"Gwanggil","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Embedded Systems Engineering, Incheon National UniversityAcademyro-119, Incheon 22012, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2986-1045","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China"}]},{"given":"Xiaomin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","article-title":"Image fusion meets deep learning: A survey and perspective","volume":"76","author":"Zhang","year":"2021","journal-title":"Inf. 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