# EMEF: Ensemble Multi-Exposure Image Fusion

arXiv: https://arxiv.org/abs/2305.12734 · HTML: https://mlsanity.com/paper/2305.12734/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 22 May 2023.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Generalization and Equilibrium in Generative Adversarial Nets (GANs)](https://mlsanity.com/paper/1703.00573/) (2017)
- [DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs](https://mlsanity.com/paper/1712.07384/) (2017)
- [Benchmarking and Comparing Multi-exposure Image Fusion Algorithms](https://mlsanity.com/paper/2007.15156/) (2020)
- [TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning](https://mlsanity.com/paper/2112.01030/) (2021)
- [GAN Ensemble for Anomaly Detection](https://mlsanity.com/paper/2012.07988/) (2020)

## Built on by (most cited first, 10 of 10)

- [UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion](https://mlsanity.com/paper/2501.11515/) (2025)
- [Learn2Talk: 3D Talking Face Learns from 2D Talking Face](https://mlsanity.com/paper/2404.12888/) (2024)
- [FaceRefiner: High-Fidelity Facial Texture Refinement with Differentiable Rendering-based Style Transfer](https://mlsanity.com/paper/2601.04520/) (2026)
- [CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization](https://mlsanity.com/paper/2603.19121/) (2026)
- [Multi-exposure HDR Imaging: A Review of Pixel-level and Feature-level Reconstruction Methods](https://mlsanity.com/paper/2608.28674/) (2026)
- [EfficienT-HDR: An Efficient Transformer-Based Framework via Multi-Exposure Fusion for HDR Reconstruction](https://mlsanity.com/paper/2509.19779/) (2025)
- [GaussianSwap: Animatable Video Face Swapping with 3D Gaussian Splatting](https://mlsanity.com/paper/2601.05511/) (2026)
- [ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion](https://mlsanity.com/paper/2607.03110/) (2026)
- [Dual-Output Multi-Exposure HDR Reconstruction via SDR Fusion and Gain Map Inverse Tone Mapping](https://mlsanity.com/paper/2608.05626/) (2026)
- [AutoLumNet: Monotone Optimal Transport for Single-Shot Exposure Correction](https://mlsanity.com/paper/2608.19860/) (2026)

## Abstract (arXiv)

Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don't utilize any synthesized ground truth, design any loss function, or develop any fusion strategy. Our proposed method EMEF takes advantage of the wisdom of multiple imperfect MEF contributors including both conventional and deep learning-based methods. Specifically, EMEF consists of two main stages: pre-train an imitator network and tune the imitator in the runtime. In the first stage, we make a unified network imitate different MEF targets in a style modulation way. In the second stage, we tune the imitator network by optimizing the style code, in order to find an optimal fusion result for each input pair. In the experiment, we construct EMEF from four state-of-the-art MEF methods and then make comparisons with the individuals and several other competitive methods on the latest released MEF benchmark dataset. The promising experimental results demonstrate that our ensemble framework can "get the best of all worlds". The code is available at https://github.com/medalwill/EMEF.