MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstruction

Poster Video	Poster PDF

Getting Started

MetaHDR was implemented and tested on CentOS 7 with python >=3.9.0, and supports GPU and CPU computation. We have also provided a Colab demo for convenience.

First, clone the repository

git clone https://github.com/edwin-pan/MetaHDR.git

Next, install the requirements

pip install requirements.txt

Running the Demo

The demo code provided runs MetaHDR on any LDR image input. We have provided a sample scene input directory at ./scene_demo. LDR images need to be placed in ./scene_demo/LDR/ and the corresponding HDR labels should go to ./scene_demo/HDR/.

To run the demo,

python3 demo.py --input_folder ./scene_demo --output_folder ./scene_demo/output

A new directory at ./scene_demo/output/ will contain the output HDR image.

Running the Evaluation

Make sure that data is downloaded and formatted correctly (see data.md).

To run evaluation,

python3 eval.py --model_dir <PATH-TO-TRAINING-OUTPUTS> --cfg <PATH-TO-CONFIG>

Running the Training

Make sure that data is downloaded and formatted correctly (see data.md). Training scripts will log the

To run training,

python3 train.py --cfg <PATH-TO-CONFIG>

Google Colab

Acknowledgement

This work was completed as a Final Project for EE 367 / CS 448I: Computational Imaging and Display at Stanford University. We would like to thank our professor, Dr. Gordon Wetzstein, for his valuable instruction throughout the quarter and our project mentor, Cindy Nguyen, for giving us insights into our project.

Citation

@inproceedings{metahdr2021,
  title={MetaHDR: Model-Agnostic Meta-Learning for HDR Image Reconstruction},
  author={Pan, Edwin and Vento, Anthony},
  month={March},
  year={2021}
}

References

A full list of references for this project can be found in the arxiv paper.