RFN-Nest: An end-to-end residual fusion network for infrared and visible images
This work addresses a specific problem in image fusion for applications like surveillance or medical imaging, but it is incremental as it builds on existing deep learning techniques with novel architectural and training components.
The authors tackled the challenge of designing learnable fusion strategies for infrared and visible image fusion by developing RFN-Nest, an end-to-end residual fusion network. The method outperformed state-of-the-art approaches in both subjective and objective evaluations on public datasets.
In the image fusion field, the design of deep learning-based fusion methods is far from routine. It is invariably fusion-task specific and requires a careful consideration. The most difficult part of the design is to choose an appropriate strategy to generate the fused image for a specific task in hand. Thus, devising learnable fusion strategy is a very challenging problem in the community of image fusion. To address this problem, a novel end-to-end fusion network architecture (RFN-Nest) is developed for infrared and visible image fusion. We propose a residual fusion network (RFN) which is based on a residual architecture to replace the traditional fusion approach. A novel detail-preserving loss function, and a feature enhancing loss function are proposed to train RFN. The fusion model learning is accomplished by a novel two-stage training strategy. In the first stage, we train an auto-encoder based on an innovative nest connection (Nest) concept. Next, the RFN is trained using the proposed loss functions. The experimental results on public domain data sets show that, compared with the existing methods, our end-to-end fusion network delivers a better performance than the state-of-the-art methods in both subjective and objective evaluation. The code of our fusion method is available at https://github.com/hli1221/imagefusion-rfn-nest