# VIFB: A Visible and Infrared Image Fusion Benchmark

arXiv: https://arxiv.org/abs/2002.03322 · HTML: https://mlsanity.com/paper/2002.03322/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 25 of 25)

- [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)
- [Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption](https://mlsanity.com/paper/2501.10761/) (2025)
- [Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond](https://mlsanity.com/paper/2503.01210/) (2025)
- [MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Training](https://mlsanity.com/paper/2404.11016/) (2024)
- [FCDFusion: a Fast, Low Color Deviation Method for Fusing Visible and Infrared Image Pairs](https://mlsanity.com/paper/2408.01080/) (2024)
- [Conditional Controllable Image Fusion](https://mlsanity.com/paper/2411.01573/) (2024)
- [Instruction-Driven Fusion of Infrared-Visible Images: Tailoring for Diverse Downstream Tasks](https://mlsanity.com/paper/2411.09387/) (2024)
- [ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation](https://mlsanity.com/paper/2204.12367/) (2022)
- [A Multi-scale Information Integration Framework for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2312.04328/) (2023)
- [Bridging Human Evaluation to Infrared and Visible Image Fusion](https://mlsanity.com/paper/2603.03871/) (2026)
- [ShadowSense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection from RGB-Thermal Drone Imagery](https://mlsanity.com/paper/2310.16212/) (2023)
- [Multimodal Image Fusion based on Hybrid CNN-Transformer and Non-local Cross-modal Attention](https://mlsanity.com/paper/2210.09847/) (2022)
- [FusionSAM: Visual Multi-Modal Learning with Segment Anything](https://mlsanity.com/paper/2408.13980/) (2024)
- [Visible and Infrared Image Fusion Using Encoder-Decoder Network](https://mlsanity.com/paper/2412.08073/) (2024)
- [Customized Fusion: A Closed-Loop Dynamic Network for Adaptive Multi-Task-Aware Infrared-Visible Image Fusion](https://mlsanity.com/paper/2604.08924/) (2026)
- [Certifiable Artificial Intelligence Through Data Fusion](https://mlsanity.com/paper/2111.02001/) (2021)
- [Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion](https://mlsanity.com/paper/2112.02869/) (2021)
- [Explicit and implicit models in infrared and visible image fusion](https://mlsanity.com/paper/2206.09581/) (2022)
- [Self-Supervised RGB-T Tracking with Cross-Input Consistency](https://mlsanity.com/paper/2301.11274/) (2023)
- [Fusion of Infrared and Visible Images based on Spatial-Channel Attentional Mechanism](https://mlsanity.com/paper/2308.13672/) (2023)
- [MultiTaskVIF: Segmentation-oriented visible and infrared image fusion via multi-task learning](https://mlsanity.com/paper/2505.06665/) (2025)
- [Causality-Driven Infrared and Visible Image Fusion](https://mlsanity.com/paper/2505.20830/) (2025)
- [SSVIF: Self-Supervised Segmentation-Oriented Visible and Infrared Image Fusion](https://mlsanity.com/paper/2509.22450/) (2025)
- [EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2603.16130/) (2026)
- [Multimodal Object Detection Under Sparse Forest-Canopy Occlusion](https://mlsanity.com/paper/2605.15326/) (2026)

## Abstract (arXiv)

Visible and infrared image fusion is one of the most important areas in image processing due to its numerous applications. While much progress has been made in recent years with efforts on developing fusion algorithms, there is a lack of code library and benchmark which can gauge the state-of-the-art. In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we present a visible and infrared image fusion benchmark (VIFB) which consists of 21 image pairs, a code library of 20 fusion algorithms and 13 evaluation metrics. We also carry out large scale experiments within the benchmark to understand the performance of these algorithms. By analyzing qualitative and quantitative results, we identify effective algorithms for robust image fusion and give some observations on the status and future prospects of this field.