Details

Autor(en) / Beteiligte

• Li, Hui
• Wu, Xiao-Jun

Titel

DenseFuse: A Fusion Approach to Infrared and Visible Images

Ist Teil von

• IEEE transactions on image processing, 2019-05, Vol.28 (5), p.2614-2623

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2019

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.