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Infrared and visible image fusion algorithm based on a cross-layer densely connected convolutional network

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Abstract

To preserve the saliency of targets in infrared images and the textures in visible images, a novel infrared and visible image fusion method, to the best of our knowledge, is proposed. First, we design a densely connected convolutional network that contains an encoder, fusion, and decoder to minimize the omission of source image effective information. Then, a loss function based on the variational model is designed to retain the thermal radiation information of the infrared image and the details of the visible image to the greatest extent. The experimental results show that the proposed method outperforms state-of-the-art methods in terms of six metrics and better preserves the clear target and textures of infrared and visible images.

© 2022 Optica Publishing Group

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Data availability

Data for the TNO Image Fusion Dataset is available in Ref. [22]. Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

22. A. Toet, “TNO Image Fusion Dataset,” figshare, 2014, https://figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029

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