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Article type: Research Article
Authors: Hu, Huixiana; * | Wang, Xiub | Li, Tiana
Affiliations: [a] Department of Medical Engineering, WanNan Medical College, City WuHu, China | [b] Centre of Combination of TCM and West Medicine WanNan Medical College, City WuHu, China
Correspondence: [*] Corresponding author. Huixian Hu, Department of Medical Engineering, WanNan Medical College, City WuHu, China. E-mail: huixianhu4@gmail.com.
Abstract: In the IP sector, the combination of visible image fusion (VIF) with infrared (IR) gives a more comprehensive and accurate description of a target image. To get over the problems of detail and energy loss during the fusion process caused by current deep learning fusion approaches, it is proposed to use a fusion strategy of IR and visible pictures based on full convolutional network (FCN) applying transfer learning. FCN model can take any size of the input and generate constant size of the output with desired rules. Through effective inference and learning procedure, the ability of features extraction and energy conservation can be enhanced a lot. Experimental results demonstrate that the suggested method succeeds in improving IF quality over the other two comparable methods by preserving high light intensity and retrieving detail information. This also confirms its dominance across five different objective assessment indices: mutual information (MI), entropy (EN), edge-based similarity measure (Qabf), sum of correlations of differences (SCD), and multi-scale structural similarity for image (MS-SSIM).
Keywords: Image fusion, full convolutional network, transfer learning, zero-phase component analysis
DOI: 10.3233/JIFS-236094
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2825-2834, 2024
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