期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 28, 期 5, 页码 2614-2623出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2018.2887342
关键词
Image fusion; deep learning; dense block; infrared image; visible image
资金
- National Natural Science Foundation of China [61672265, U1836218, 61876072]
- 111 Project of Ministry of Education of China [B12018]
In this paper, we present a novel deep learning architecture for infrared and visible images fusion problems. In contrast to conventional convolutional networks, our encoding network is combined with convolutional layers, a fusion layer, and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in the encoding process, and two fusion layers (fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by a decoder. Compared with existing fusion methods, the proposed fusion method achieves the state-of-the-art performance in objective and subjective assessment.
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