Code related to the paper "MobileMEF: Fast and Efficient Method for Multi-Exposure Fusion"
We recommend using Conda as package manager.
conda env create -f environment.yml
The model.py
file provides tools for inference and converting the model to TFLITE or ONNX format.
The h5/
folder provides checkpoints for the trained models using EVs 1 and -1 (sice_ev1.h5
), and most under and over exposed frames (sice_ev_most.h5
).
The data/
folder provides examples of images for the input pipeline.
The utils/
folder comprises auxiliary code for metrics evaluation and benchmarking with ONNX model format.
If this work has been helpful to you, we would appreciate it if you could cite our paper!
@article{kirsten2025mobilemef,
title={MobileMEF: fast and efficient method for real-time mobile multi-exposure fusion},
author={Kirsten, Lucas Nedel and Fu, Zhicheng and Madhusudhana, Nikhil Ambha},
journal={Journal of Real-Time Image Processing},
volume={22},
number={1},
pages={1--15},
year={2025},
publisher={Springer}
}