MobileMEF

Code related to the paper "MobileMEF: Fast and Efficient Method for Multi-Exposure Fusion"

Preview of MobileMEF

Usage

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.

Visual Results

Citation

If this work has been helpful to you, we would appreciate it if you could cite our paper!

@misc{kirsten2024mobilemeffastefficientmethod,
      title={MobileMEF: Fast and Efficient Method for Multi-Exposure Fusion}, 
      author={Lucas Nedel Kirsten and Zhicheng Fu and Nikhil Ambha Madhusudhana},
      year={2024},
      eprint={2408.07932},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2408.07932}, 
}