Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

Overview

CDVD-TSPNL

LICENSE Python PyTorch

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

By Jinshan Pan, Boming Xu, and Haoran Bai

This repository is the official PyTorch implementation of "Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity"

Updates

[2022-02-08] Training code and Testing code are available!
[2022-02-07] Paper coming soon...

Experimental Results

Quantitative evaluations on the video deblurring dataset [11] in terms of PSNR and SSIM. All the comparison results are generated using the publicly available code. All the restored frames instead of randomly selected 30 frames from each test set [11] are used for evaluations. DVD

Quantitative evaluations on the GoPro dataset [43] in terms of PSNR and SSIM. * denotes the reported results from [47]. GOPRO

Quantitative evaluations on the BSD video deblurring dataset [5] in terms of PSNR and SSIM. BSD

Dependencies

  • Linux (Tested on Ubuntu 18.04)
  • Python 3 (Recommend to use Anaconda)
  • PyTorch 1.8.0: conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
  • Install dependent packages :pip install -r requirements.txt
  • Install CDVD-TSPNL :python setup.py develop

Get Started

Pretrained models

  • Models are available in './experiments/pretrained_models/'

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

  • FlowNet pretrained model has been downloaded in './pretrained_models/flownet/'
  • 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

  • Model are available in './experiments/pretrained_models/'
  • 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
  • The deblured result will be in './results/'.
  • We calculate PSNRs/SSIMs following [Here]
  • If we set flip_seq: Ture in config files, testing code will use self-ensemble strategy.(CDVDTSPNL+)

Citation

Owner
hippopmonkey
hippopmonkey
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