Jinshan Pan, Long Sun, Boming Xu, Jiangxin Dong, and Jinhui Tang
IMAG Lab, Nanjing University of Science and Technology

This repo is a official implementation of "Learning Efficient Deep Discriminative Spatial and Temporal Networks for Video Deblurring".

DSTNet+ is an extension of DSTNet.

• 2025.03.25: All pretrained models and visual results are available.
• 2025.03.25: The paper can be found here.
• 2025.03.14: This paper is accepted by TPAMI.
• 2024.01.08: This repo is created.

• Python 3.9, PyTorch == 1.13
• BasicSR 1.4.2
• Platforms: Ubuntu 18.04, cuda-11

git clone https://github.com/sunny2109/DSTNet-plus.git
cd DSTNet-plus
conda create -n dstnetplus python=3.9
conda activate dstnetplus
# Install dependent packages
pip install -r requirements.txt
# Install cupy
# Please make sure that the installed Cupy version matches your existing CUDA installation!
pip install cupy-cuda11x
# Install BasicSR
python setup.py develop

Used training and testing sets can be downloaded as follows:

# train DSTNetPlus on GoPro dataset
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 python basicsr/train.py -opt options/train/train_base_GoPro.yml --launcher pytorch

# train DSTNetPlus on DVD dataset
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 python basicsr/train.py -opt options/train/train_base_DVD.yml --launcher pytorch

# train DSTNetPlus on BSD dataset
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 python basicsr/train.py -opt options/train/train_base_BSD1ms.yml --launcher pytorch

• Download the pretrained models.

Please download the pretrained and put it in ./checkpoints/.

• Download the testing dataset.

Please download the test dataset and put it in ./datasets/.

• Run the following commands:

python basicsr/test.py -opt options/test/test_base_GoPro.yml
cd results
python merge_full.py

• The test results will be in './results'.

We achieve SOTA performance on a set of blurring datasets. Detailed results can be found in the paper. All visual results of DSTNetPlus can be downloaded here.

If you have any questions, please feel free to reach us out at cs.longsun@gmail.com

If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝

@article{DSTNetPlus,
  title={Learning Efficient Deep Discriminative Spatial and Temporal Networks for Video Deblurring},
  author={Pan, Jinshan and Sun, Long and Xu Boming and Dong, Jiangxin and Tang, Jinhui},
  journal={TPAMI},
  year={2025}
}