DenseFuse: A Fusion Approach to Infrared and Visible Images

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...

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Veröffentlicht in:IEEE transactions on image processing Jg. 28; H. 5; S. 2614 - 2623
Hauptverfasser: Li, Hui, Wu, Xiao-Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Architecture
Artificial neural networks
Coding
Decoding
deep learning
dense block
Feature extraction
Image coding
Image contrast
Image fusion
Image reconstruction
infrared image
Infrared imagery
Machine learning
Subjective assessment
Training
visible image
ISSN:1057-7149, 1941-0042, 1941-0042
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2018.2887342