MLDNet: Multi-level dense network for multi-focus image fusion |
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| Affiliation: | 1. Key Laboratory of Electronic Equipment Structure Design, Ministry of Education, Xidian University, Xi''an, Shaanxi 710071,China;2. Center for Complex Systems, School of Mechano-electronic Engineering, Xidian University, Xi''an Shaanxi 710071, China;3. WMG Data Science, University of Warwick, Coventry CV4 AL7, U.K;1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;1. School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;2. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China |
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| Abstract: | In this study, we propose a new deep learning architecture named Multi-Level Dense Network (MLDNet) for multi-focus image fusion (MFIF). We introduce shallow and dense feature extraction in our feature extraction module to extract images features in a more robust way. In particular, we extracted the features from a mixture of many distributions from prior to the complex distribution through densely connected convolutional layers, then the extracted features are fused to form dense local feature maps. We added global feature fusion into the proposed architecture in order to merge the dense local feature maps of each source image into a fused image representation for the reconstruction of the final fused image. Our proposed MLDNet learns feature extraction, feature fusion and reconstruction within the same network to provide an end-to-end solution for MFIF. Experimental results demonstrate that our proposed method achieved significant performance against different state-of-the-art MFIF methods. |
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| Keywords: | Multi-focus image fusion Deep learning Feature extraction Feature fusion |
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