IEEE Transactions on Image Processing· 2018Q1
DenseFuse: A Fusion Approach to Infrared and Visible Images
- 1,878citations
- Q1SCImago
- 2018year
Short summary
A novel deep learning architecture, DenseFuse, has been developed to fuse infrared and visible images, achieving state-of-the-art performance.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces DenseFuse, a deep learning architecture for infrared and visible image fusion.
- Employs a novel encoding network with dense blocks for enhanced feature extraction.
- Utilizes two fusion strategies to combine features from source images.
- Achieves state-of-the-art performance compared to existing fusion methods.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem. In contrast to conventional convolutional networks, our encoding network is combined by convolutional layers, 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 encoding process. And two fusion layers(fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by decoder. Compared with existing fusion methods, the proposed fusion method achieves state-of-the-art performance in objective and subjective assessment.
The authors' abstract, as published at the source. IEEE Transactions on Image Processing, 2018 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Media Technology
Media TechnologyEngineering