# DAF-Net: A Dual-Branch Feature Decomposition Fusion Network with Domain Adaptive for Infrared and Visible Image Fusion

arXiv: https://arxiv.org/abs/2409.11642 · HTML: https://mlsanity.com/paper/2409.11642/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images](https://mlsanity.com/paper/2204.11436/) (2022)

## Built on by (most cited first, 1 of 1)

- [Transformer-Based Dual-Optical Attention Fusion Crowd Head Point Counting and Localization Network](https://mlsanity.com/paper/2505.06937/) (2025)

## Abstract (arXiv)

Infrared and visible image fusion aims to combine complementary information from both modalities to provide a more comprehensive scene understanding. However, due to the significant differences between the two modalities, preserving key features during the fusion process remains a challenge. To address this issue, we propose a dual-branch feature decomposition fusion network (DAF-Net) with domain adaptive, which introduces Multi-Kernel Maximum Mean Discrepancy (MK-MMD) into the base encoder and designs a hybrid kernel function suitable for infrared and visible image fusion. The base encoder built on the Restormer network captures global structural information while the detail encoder based on Invertible Neural Networks (INN) focuses on extracting detail texture information. By incorporating MK-MMD, the DAF-Net effectively aligns the latent feature spaces of visible and infrared images, thereby improving the quality of the fused images. Experimental results demonstrate that the proposed method outperforms existing techniques across multiple datasets, significantly enhancing both visual quality and fusion performance. The related Python code is available at https://github.com/xujian000/DAF-Net.