DenseFuse: A Fusion Approach to Infrared and Visible Images
This addresses image fusion for applications like surveillance or medical imaging, but it is incremental as it builds on existing deep learning methods with architectural modifications.
The paper tackles the problem of fusing infrared and visible images by proposing DenseFuse, a deep learning architecture that achieves state-of-the-art performance in both objective and subjective assessments.
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. Code and pre-trained models are available at https://github.com/hli1221/imagefusion_densefuse