DSTNet

Paper | Supp | Discussion

Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring

By Jinshan Pan*, Boming Xu*, Jiangxin Dong, Jianjun Ge and Jinhui Tang

Abstract: How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. We show that the proposed method does not require additional alignment methods and performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity.

This repository is the official PyTorch implementation of our CVPR2023 paper "Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring".

Network Architecture

Updates

[2022-02-28] Paper has been accepted by CVPR2023
[2023-03-25] Training & Testing code is available!

Experimental Results

Quantitative evaluations on the GoPro dataset. “Ours-L” denotes a large model, where we use 96 features and 30 ResBlocks in the DTFF module.

Quantitative evaluations on the DVD dataset in terms of PSNR and SSIM.

Quantitative evaluations on the BSD deblurring dataset in terms of PSNR and SSIM.

Dependencies

Linux (Tested on Ubuntu 18.04)
Python 3 (Recommend to use Anaconda)
PyTorch 1.10.1: conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
Install dependent packages :pip install -r requirements.txt
Install DSTNet :python setup.py develop

Get Started

Pretrained models

Models are available in './experiments/model_name'

Dataset Organization Form

If you prepare your own dataset, please follow the following form like GOPRO/DVD:

|--dataset  
    |--blur  
        |--video 1
            |--frame 1
            |--frame 2
                ：  
        |--video 2
            :
        |--video n
    |--gt
        |--video 1
            |--frame 1
            |--frame 2
                ：  
        |--video 2
        	:
        |--video n

Training

Download training dataset like above form.
Run the following commands:

Single GPU
python basicsr/train.py -opt options/train/Deblur/train_Deblur_GOPRO.yml
Multi-GPUs
python -m torch.distributed.launch --nproc_per_node=8 --master_port=4321 basicsr/train.py -opt options/train/Deblur/train_Deblur_GOPRO.yml --launcher pytorch

Testing

Models are available in './experiments/'.
Organize your dataset(GOPRO/DVD/BSD) like the above form.
Run the following commands:

python basicsr/test.py -opt options/test/Deblur/test_Deblur_GOPRO.yml
cd results
python merge_full.py
python calculate_psnr.py

Before running merge_full.py, you should change the parameters in this file of Line 5,6,7,8.
The deblured result will be in './results/dataset_name/'.
Before running calculate_psnr.py, you should change the parameters in this file of Line 5,6.
We calculate PSNRs/SSIMs by running calculate_psnr.py

Citation

@InProceedings{Pan_2023_CVPR,
    author = {Pan, Jinshan and Xu, Boming and Dong, Jiangxin and Ge, Jianjun and Tang, Jinhui},
    title = {Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition(CVPR)},
    month = {Feb},
    year = {2023}
}

xuboming8 / DSTNet