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XinyiYing
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Description

Repository for "Deformable 3D Convolution for Video Super-Resolution", SPL, 2020

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Deformable 3D Convolution for Video Super-Resolution

Pytorch implementation of deformable 3D convolution network (D3Dnet). [PDF]

Our code is based on cuda and can perform deformation in any dimension of 3D convolution.

Overview

Architecture of D3Dnet


Architecture of D3D


Requirements

  • Python 3
  • pytorch (1.0.0), torchvision (0.2.2) / pytorch (1.2.0), torchvision (0.4.0)
  • numpy, PIL
  • Visual Studio 2015

Build

Compile deformable 3D convolution:
1. Cd to

code/dcn
. 2. For Windows users, run
cmd make.bat
. For Linux users, run
bash make.sh
. The scripts will build D3D automatically and create some folders. 3. See
test.py
for example usage.

Datasets

Training dataset

  1. Download the Vimeo dataset and put the images in
    code/data/Vimeo
    .
  2. Cd to
    code/data/Vimeo
    and run
    generate_LR_Vimeo90K.m
    to generate training data as below:
    Vimeo
    └── sequences
           ├── 00001
           ├── 00002
           ├── ...
    └── LR_x4
           ├── 00001
           ├── 00002
           ├── ...        
    ├── readme.txt 
    ├── sep_trainlist.txt
    ├── sep_testlist.txt
    └── generate_LR_Vimeo90K.m      
    

Test dataset

  1. Download the dataset Vid4 and SPMC-11 dataset in https://pan.baidu.com/s/1PKZeTo8HVklHU5Pe26qUtw (Code: 4l5r) and put the folder in
    code/data
    .
  2. (optional) You can also download Vid4 and SPMC-11 or other video datasets and prepare test data in
    code/data
    as below:
    data
    └── dataset_1
         └── scene_1
               └── hr    
                  ├── hr_01.png  
                  ├── hr_02.png  
                  ├── ...
                  └── hr_M.png    
               └── lr_x4
                  ├── lr_01.png  
                  ├── lr_02.png  
                  ├── ...
                  └── lr_M.png   
         ├── ...        
         └── scene_M
    ├── ...    
    └── dataset_N      
    
    ## Results

Quantitative Results

Table 1. PSNR/SSIM achieved by different methods.

Table 2. Temporal consistency and computational efficiency achieved by different methods.

We have organized the Matlab code framework of Video Quality Assessment metric T-MOVIE and MOVIE. [Code]
Welcome to have a look and use our code.

Qualitative Results

Qualitative results achieved by different methods. Blue boxes represent the temporal profiles among different frames.

A demo video is available at https://wyqdatabase.s3-us-west-1.amazonaws.com/D3Dnet.mp4

Citiation

@article{D3Dnet,
  author = {Ying, Xinyi and Wang, Longguang and Wang, Yingqian and Sheng, Weidong and An, Wei and Guo, Yulan},
  title = {Deformable 3D Convolution for Video Super-Resolution},
  journal = {IEEE Signal Processing Letters},
  volume = {27},
  pages = {1500-1504},
  year = {2020},
}

Acknowledgement

This code is built on [DCNv2] and [SOF-VSR]. We thank the authors for sharing their codes.

Contact

Please contact us at [email protected] for any question.

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