{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:22:25Z","timestamp":1770830545172,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2018B010109001"],"award-info":[{"award-number":["2018B010109001"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["No. 2020B1111010002"],"award-info":[{"award-number":["No. 2020B1111010002"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019B020214001"],"award-info":[{"award-number":["2019B020214001"]}]},{"name":"Guangdong Marine Economic Development Project (GDNRC)","award":["[2020]018"],"award-info":[{"award-number":["[2020]018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning (DL) has achieved significant attention in the field of infrared (IR) and visible (VI) image fusion, and several attempts have been made to enhance the quality of the final fused image. It produces better results than conventional methods; however, the captured image cannot acquire useful information due to environments with poor lighting, fog, dense smoke, haze, and the noise generated by sensors. This paper proposes an adaptive fuzzy-based preprocessing enhancement method that automatically enhances the contrast of images with adaptive parameter calculation. The enhanced images are then decomposed into base and detail layers by anisotropic diffusion-based edge-preserving filters that remove noise while smoothing the edges. The detailed parts are fed into four convolutional layers of the VGG-19 network through transfer learning to extract features maps. These features maps are fused by multiple fusion strategies to obtain the final fused detailed layer. The base parts are fused by the PCA method to preserve the energy information. Experimental results reveal that our proposed method achieves state-of-the-art performance compared with existing fusion methods in a subjective evaluation through the visual experience of experts and statistical tests. Moreover, the objective assessment parameters are conducted by various parameters (FMI, SSIMa, API, EN, QFAB, and NFAB) which were used in the comparison method. The proposed method achieves 0.2651 to 0.3951, 0.5827 to 0.8469, 56.3710 to 71.9081, 4.0117 to 7.9907, and 0.6538 to 0.8727 gain for FMI, SSIMa, API, EN, and QFAB, respectively. At the same time, the proposed method has more noise reduction (0.3049 to 0.0021) that further justifies the efficacy of the proposed method than conventional methods.<\/jats:p>","DOI":"10.3390\/rs14040939","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T22:44:47Z","timestamp":1644965087000},"page":"939","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["An Improved Infrared and Visible Image Fusion Using an Adaptive Contrast Enhancement Method and Deep Learning Network with Transfer Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Jameel Ahmed","family":"Bhutto","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"}]},{"given":"Lianfang","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory Zhuhai, Zhuhai 519080, China"},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China"}]},{"given":"Qiliang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory Zhuhai, Zhuhai 519080, China"},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9293-5625","authenticated-orcid":false,"given":"Zhengzheng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"}]},{"given":"Lubin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8560-0026","authenticated-orcid":false,"given":"Toufique Ahmed","family":"Soomro","sequence":"additional","affiliation":[{"name":"Electronic Engineering Department, Quaid-e-Awam University of Science and Technology, Nawabshah 67480, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"ref_1","first-page":"436","article-title":"Infrared and Visible Image Fusion Based on Semantic Segmentation","volume":"58","author":"Zhou","year":"2021","journal-title":"J. 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