# Benchmarking and Comparing Multi-exposure Image Fusion Algorithms

arXiv: https://arxiv.org/abs/2007.15156 · HTML: https://mlsanity.com/paper/2007.15156/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 30 of 33)

- [Task-Customized Mixture of Adapters for General Image Fusion](https://mlsanity.com/paper/2403.12494/) (2024)
- [Image Fusion via Vision-Language Model](https://mlsanity.com/paper/2402.02235/) (2024)
- [TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning](https://mlsanity.com/paper/2112.01030/) (2021)
- [UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion](https://mlsanity.com/paper/2501.11515/) (2025)
- [ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning](https://mlsanity.com/paper/2312.07943/) (2023)
- [Efficient Rectified Flow for Image Fusion](https://mlsanity.com/paper/2509.16549/) (2025)
- [Mutual-Guided Dynamic Network for Image Fusion](https://mlsanity.com/paper/2308.12538/) (2023)
- [Conditional Controllable Image Fusion](https://mlsanity.com/paper/2411.01573/) (2024)
- [EMEF: Ensemble Multi-Exposure Image Fusion](https://mlsanity.com/paper/2305.12734/) (2023)
- [MMAIF: Multi-task and Multi-degradation All-in-One for Image Fusion with Language Guidance](https://mlsanity.com/paper/2503.14944/) (2025)
- [Test-Time Dynamic Image Fusion](https://mlsanity.com/paper/2411.02840/) (2024)
- [UniFusion: A Unified Image Fusion Framework with Robust Representation and Source-Aware Preservation](https://mlsanity.com/paper/2603.14214/) (2026)
- [Task-Generalized Adaptive Cross-Domain Learning for Multimodal Image Fusion](https://mlsanity.com/paper/2508.15505/) (2025)
- [Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond](https://mlsanity.com/paper/2410.12274/) (2024)
- [Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion](https://mlsanity.com/paper/2503.07235/) (2025)
- [A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration](https://mlsanity.com/paper/2312.10604/) (2023)
- [Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion](https://mlsanity.com/paper/2404.06033/) (2024)
- [Self-supervised Multiplex Consensus Mamba for General Image Fusion](https://mlsanity.com/paper/2512.20921/) (2025)
- [IllumFlow: Illumination-Adaptive Low-Light Enhancement via Conditional Rectified Flow and Retinex Decomposition](https://mlsanity.com/paper/2511.02411/) (2025)
- [Multi-exposure HDR Imaging: A Review of Pixel-level and Feature-level Reconstruction Methods](https://mlsanity.com/paper/2608.28674/) (2026)
- [Model and Evaluation: Towards Fairness in Multilingual Text Classification](https://mlsanity.com/paper/2303.15697/) (2023)
- [Dig2DIG: Dig into Diffusion Information Gains for Image Fusion](https://mlsanity.com/paper/2503.18627/) (2025)
- [RLBayes: a Bayesian Network Structure Learning Algorithm via Reinforcement Learning-Based Search Strategy](https://mlsanity.com/paper/2504.05167/) (2025)
- [Rethinking Few-Shot Image Fusion: Granular Ball Priors Enable General-Purpose Deep Fusion](https://mlsanity.com/paper/2504.08937/) (2025)
- [MultiTaskVIF: Segmentation-oriented visible and infrared image fusion via multi-task learning](https://mlsanity.com/paper/2505.06665/) (2025)
- [Multi-Grained Text-Guided Image Fusion for Multi-Exposure and Multi-Focus Scenarios](https://mlsanity.com/paper/2512.20556/) (2025)
- [A Hardware-Algorithm Co-Designed Framework for HDR Imaging and Dehazing in Extreme Rocket Launch Environments](https://mlsanity.com/paper/2601.08162/) (2026)
- [Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion](https://mlsanity.com/paper/2605.00461/) (2026)
- [ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion](https://mlsanity.com/paper/2607.03110/) (2026)
- [Coarse-to-fine Framework for Generative MEF via Implicit Neural Representation](https://mlsanity.com/paper/2607.17611/) (2026)

## Abstract (arXiv)

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to multi-exposure image fusion. However, although much efforts have been made on developing MEF algorithms, the lack of benchmark makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus significantly hindering the development of this field. In this paper, we fill this gap by proposing a benchmark for multi-exposure image fusion (MEFB) which consists of a test set of 100 image pairs, a code library of 16 algorithms, 20 evaluation metrics, 1600 fused images and a software toolkit. To the best of our knowledge, this is the first benchmark in the field of multi-exposure image fusion. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare performances and investigate MEF algorithms.