# DFVO: Learning Darkness-free Visible and Infrared Image Disentanglement and Fusion All at Once

arXiv: https://arxiv.org/abs/2505.04526 · HTML: https://mlsanity.com/paper/2505.04526/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 59 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [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)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [Guided Image Generation with Conditional Invertible Neural Networks](https://mlsanity.com/paper/1907.02392/) (2019)
- [Enhanced Invertible Encoding for Learned Image Compression](https://mlsanity.com/paper/2108.03690/) (2021)
- [Invertible Image Rescaling](https://mlsanity.com/paper/2005.05650/) (2020)
- [Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration](https://mlsanity.com/paper/2408.15994/) (2024)
- [SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images](https://mlsanity.com/paper/2204.11436/) (2022)
- [SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection](https://mlsanity.com/paper/2403.05416/) (2024)
- [NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes](https://mlsanity.com/paper/2402.18172/) (2024)
- [Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Prior](https://mlsanity.com/paper/2406.00632/) (2024)
- [CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond](https://mlsanity.com/paper/2502.14493/) (2025)
- [Invertible Network for Classification and Biomarker Selection for ASD](https://mlsanity.com/paper/1907.09729/) (2019)
- [UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion](https://mlsanity.com/paper/2101.04506/) (2021)

## Built on by (most cited first, 3 of 3)

- [AngularFuse: A Closer Look at Angle-based Perception for Spatial-Sensitive Multi-Modality Image Fusion](https://mlsanity.com/paper/2510.12260/) (2025)
- [UMSS: Towards Unsupervised Multi-modal Semantic Segmentation](https://mlsanity.com/paper/2607.12372/) (2026)
- [Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement](https://mlsanity.com/paper/2607.13925/) (2026)

## Abstract (arXiv)

Visible and infrared image fusion is one of the most crucial tasks in the field of image fusion, aiming to generate fused images with clear structural information and high-quality texture features for high-level vision tasks. However, when faced with severe illumination degradation in visible images, the fusion results of existing image fusion methods often exhibit blurry and dim visual effects, posing major challenges for autonomous driving. To this end, a Darkness-Free network is proposed to handle Visible and infrared image disentanglement and fusion all at Once (DFVO), which employs a cascaded multi-task approach to replace the traditional two-stage cascaded training (enhancement and fusion), addressing the issue of information entropy loss caused by hierarchical data transmission. Specifically, we construct a latent-common feature extractor (LCFE) to obtain latent features for the cascaded tasks strategy. Firstly, a details-extraction module (DEM) is devised to acquire high-frequency semantic information. Secondly, we design a hyper cross-attention module (HCAM) to extract low-frequency information and preserve texture features from source images. Finally, a relevant loss function is designed to guide the holistic network learning, thereby achieving better image fusion. Extensive experiments demonstrate that our proposed approach outperforms state-of-the-art alternatives in terms of qualitative and quantitative evaluations. Particularly, DFVO can generate clearer, more informative, and more evenly illuminated fusion results in the dark environments, achieving best performance on the LLVIP dataset with 63.258 dB PSNR and 0.724 CC, providing more effective information for high-level vision tasks. Our code is publicly accessible at https://github.com/DaVin-Qi530/DFVO.