3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021)

Last update: Jan 07, 2022

Related tags

Deep Learning 3D2Unet

Overview

3DDUNET

This is the code for 3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021) Conference Paper Link

Dataset

We use SMOID dataset from SMOID

Code

Prerequisites

Python 3.6
PyTorch 1.7 with GPU
opencv-python
scikit-image
tensorboard
For 3D deformable compile process please refer D3Dnet

Train and Test

Please run main.py to train and test the model

Citing

If you use any part of our research, please consider citing:

@inproceedings{zeng2021mathrm,
  title={3D2Unet:3D Deformable Unet for Low-Light Video Enhancement},
  author={Zeng, Yuhang and Zou, Yunhao and Fu, Ying},
  booktitle={Chinese Conference on Pattern Recognition and Computer Vision (PRCV)},
  pages={66--77},
  year={2021},
  organization={Springer}
}

Acknowledgement

Our work and implementations are inspired by following projects: ESTRNN SMOID D3Dnet

Owner

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3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement (PRCV2021)

Related tags

Overview

3DDUNET

Dataset

Code

Prerequisites

Train and Test

Citing

Acknowledgement

Owner

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