cvhw / dvd-sfe

Deep Video Deblurring Using Sharpness Features from Exemplars (TIP, 2020) (PyTorch)

Deep Video Deblurring Using Sharpness Features from Exemplars (Submitted to IEEE TIP, 2020)

by Xinguang Xiang, Hao Wei, Jinshan Pan

Dependencies and Installation

• Python 3 (Recommend to use Anaconda)
• PyTorch0.4.1
• Linux (Tested on Ubuntu 18.04)
• numpy
• tqdm
• imageio
• matplotlib

Dataset Preparation

We use the GOPRO_Su dataset to train our models. You can download it from here and put the dataset into 'train_dataset/'. The dataset should be organized in the following form:

|--dataset name  |--train   |--video 01    |--input     |--frame 01     |--frame 02     |...    |--GT     |--frame 01     |--frame 02     |...   |--video 02   ...  |--val  |--test

Training

• Download the FlowNet pretrained model from Baidu Drive (password:2gca) and put it into 'pretrained_model/'.
  
• Prepare the dataset same as above form.
  
• Start to train the model. Hyper parameters such as batch size, learning rate, epoch number can be tuned through command line:

python main.py --batch_size 4 --patch_size 256 --lr 1e-4 --epochs 500 --save_models

Testing

• Download our pretrained model from Baidu Drive (password:2gca) .
• The testing command is shown as follows:

python main.py --pre_train model_path --test_only

Testing your own data

• Put your testing data into 'inference/' which should be organized the same as our given examplars.
• The testing command is shown as follows:

python inference.py

Citation

@article{xiang2020DVD-SFE,
title={Deep Video Deblurring Using Sharpness Features from Exemplars},
author={Xiang, xinguang and Wei, Hao and Pan, jinshan},
journal={IEEE Transactions on Image Processing},
year={2020}
}