Semantic Scholar Open Access 2018 1697 sitasi

DenseFuse: A Fusion Approach to Infrared and Visible Images

Hui Li Xiaojun Wu

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Medicine Computer Science

Penulis (2)

Hui Li

Xiaojun Wu

Format Sitasi

APA MLA BibTeX

Li, H., Wu, X. (2018). DenseFuse: A Fusion Approach to Infrared and Visible Images. https://doi.org/10.1109/TIP.2018.2887342

Akses Cepat

Lihat di Sumber doi.org/10.1109/TIP.2018.2887342

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 1697×
Sumber Database: Semantic Scholar
DOI: 10.1109/TIP.2018.2887342
Akses: Open Access ✓