In this repository we provide code of the paper:
Improving Video Colorization by Test-Time Tuning
Yaping Zhao, Haitian Zheng, Jiebo Luo, Edmund Y. Lam
paper link: https://ieeexplore.ieee.org/abstract/document/10222579
- PyTorch (0.4.1+) torchvision
- FFmpeg (requires to be configured with --enable-libx264)
- opencv (3.4.1+)
- scikit-image
- tqdm
For information on how to install PyTorch, please refer to the PyTorch website. FFmpeg should be installed with libx264 support, which can be installed in Anaconda by using conda install x264 ffmpeg -c conda-forge.
First, download the model by running the download script:
bash download_model.sh
To reproduce the main results from our paper, simply run:
python test_vid4.py
python test_set8.py
The input video will be automatically restored and colorized based on the reference color frames using the model. If you want to perform restoration only, use --disable_colorization option.
Other options:
--gpu: Use GPU for the computation (recommended). Defaults to false.--disable_colorization: Disable colorization and only perform restoration with enhancement. Defaults to false.--mindim: Minimum edge dimension of the input video. Defaults to 320.
To prepare reference color images for your own video, it is recommended to first extract reference frames from the video using a scene detection technique such as pyscenedetect. Afterwards, colorize them by leveraging image editing software or recent interactive colorization techniques such as the Interactive Deep Colorization [Zhang et al. 2017].
- We recommend using GPU with 4GB+ memory for fast computation.
- Provided model and sample code are under a non-commercial creative commons license.
Pre-trained models are provided in the input folder. Therefore, you can quickly get started without downloading datasets.
Cite our paper if you find it interesting!
@inproceedings{zhao2023improving,
title={Improving Video Colorization by Test-Time Tuning},
author={Zhao, Yaping and Zheng, Haitian and Luo, Jiebo and Lam, Edmund Y},
booktitle={2023 IEEE International Conference on Image Processing (ICIP)},
pages={166--170},
year={2023},
organization={IEEE}
}
This code is implemented based on DeepRemaster.