# A Dual-branch Network for Infrared and Visible Image Fusion

arXiv: https://arxiv.org/abs/2101.09643 · HTML: https://mlsanity.com/paper/2101.09643/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 24 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [Mish: A Self Regularized Non-Monotonic Activation Function](https://mlsanity.com/paper/1908.08681/) (2019)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [MDLatLRR: A novel decomposition method for infrared and visible image fusion](https://mlsanity.com/paper/1811.02291/) (2018)

## Built on by (most cited first, 8 of 8)

- [FusionBooster: A Unified Image Fusion Boosting Paradigm](https://mlsanity.com/paper/2305.05970/) (2023)
- [PPT Fusion: Pyramid Patch Transformerfor a Case Study in Image Fusion](https://mlsanity.com/paper/2107.13967/) (2021)
- [FuseFormer: A Transformer for Visual and Thermal Image Fusion](https://mlsanity.com/paper/2402.00971/) (2024)
- [Visible and Infrared Image Fusion Using Encoder-Decoder Network](https://mlsanity.com/paper/2412.08073/) (2024)
- [Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion](https://mlsanity.com/paper/2102.10526/) (2021)
- [Unsupervised Image Fusion Method based on Feature Mutual Mapping](https://mlsanity.com/paper/2201.10152/) (2022)
- [Explicit and implicit models in infrared and visible image fusion](https://mlsanity.com/paper/2206.09581/) (2022)
- [Direction-aware multi-scale gradient loss for infrared and visible image fusion](https://mlsanity.com/paper/2510.13067/) (2025)

## Abstract (arXiv)

Deep learning is a rapidly developing approach in the field of infrared and visible image fusion. In this context, the use of dense blocks in deep networks significantly improves the utilization of shallow information, and the combination of the Generative Adversarial Network (GAN) also improves the fusion performance of two source images. We propose a new method based on dense blocks and GANs , and we directly insert the input image-visible light image in each layer of the entire network. We use SSIM and gradient loss functions that are more consistent with perception instead of mean square error loss. After the adversarial training between the generator and the discriminator, we show that a trained end-to-end fusion network -- the generator network -- is finally obtained. Our experiments show that the fused images obtained by our approach achieve good score based on multiple evaluation indicators. Further, our fused images have better visual effects in multiple sets of contrasts, which are more satisfying to human visual perception.