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However, most fusion approaches based on deep learning do not make effective use of the features for image fusion, which results in missing semantic content in the fused image. In this paper, a novel trustworthy image fusion method is proposed to address these issues, which applies convolutional neural networks for feature extraction and blockchain technology to protect sensitive information. The new method can effectively reduce the loss of feature information by making the output of the feature extraction network in each convolutional layer to be fed to the next layer along with the production of the previous layer, and in order to ensure the similarity between the fused image and the original image, the original input image feature map is used as the input of the reconstruction network in the image reconstruction network. Compared to other methods, the experimental results show that our proposed method can achieve better quality and satisfy human perception.<\/jats:p>","DOI":"10.1155\/2021\/6220166","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T12:23:10Z","timestamp":1625142190000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Trustworthy Image Fusion with Deep Learning for Wireless Applications"],"prefix":"10.1155","volume":"2021","author":[{"given":"Chao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Haojin","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9186-475X","authenticated-orcid":false,"given":"Yonghang","family":"Tai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8455-9332","authenticated-orcid":false,"given":"Lijun","family":"Yun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5310-0270","authenticated-orcid":false,"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2018.02.004"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3465171"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2618776"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2016.12.001"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0219691318500182"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2887342"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3417978"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcan.2020.07.003"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.2993293"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2940940"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2016.01.009"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2014.09.004"},{"key":"e_1_2_8_13_2","first-page":"386","article-title":"Image fusion based on expectation maximization algorithm and steerable pyramid","volume":"2","author":"Liu G.","year":"2004","journal-title":"Chinese Optics Letters"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.11591\/telkomnika.v11i11.2898"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2016.09.019"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.02.005"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2696055"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.OE.52.5.057006"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2885561"},{"key":"e_1_2_8_20_2","doi-asserted-by":"crossref","unstructured":"LiuY. 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