Algorithm

Linguistics

Process (computing)

Engineering

Philosophy

Convolutional code

Layer (electronics)

Operating system

Set (abstract data type)

Computer science

Code (set theory)

Decoding methods

Fusion

Organic chemistry

Source code

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Image fusion

Block (permutation group theory)

Artificial intelligence

Geometry

Encoding (memory)

Chemistry

Programming language

Deep learning

Fuse (electrical)

Convolutional neural network

Computer vision

Electrical engineering

IEEE Transactions on Image Processing

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

DenseFuse: A Fusion Approach to Infrared and Visible Images