MEF-EFormer: Efficient Transformer for Multi-Exposure Image Fusion

Official PyTorch implementation of MEF-EFormer, an efficient transformer architecture for multi-exposure image fusion.

This repository includes the code and trained weights for the MEF-EFormer method.

Paper: The paper link will be added when available.

Installation

Clone the repository:

git clone https://github.com/obouzos/MEF-EFormer.git
cd MEF-EFormer

Install the required dependencies:

pip install -r requirements.txt

Inference

The inference script expects two well-aligned multi-exposure RGB images.

Run the included example:

python inference.py --under "images/under.png" --over "images/over.png" --output "output/fused.png"

The script automatically uses CUDA when available and otherwise runs on the CPU.

Citation

Citation information will be added when the paper becomes available.