We have provided the source code of our video super-resolution, video deblurring, and video denoising models.
We provide our results at Google Cloud.
The pre-trained models are uploaded in the google cloud.
Some in-the-wild testing sequences are available here.
File Structure
Click to expand
libs
DcNv2
utils
common.py
core.py
model_opr.py
models
VDB
config.py
network.py
validate.py
sequence_test.py
load_VDB_Data.py
VideoDeblur.py
VDN
config.py
network.py
validate.py
validate_davis.py
sequence_test.py
VSR_REDS
config.py
network.py
validate.py
VSR_VIMEO90K
config.py
network.py
validate.py
sequence_test.py
The DCNv2 should be installed correctly by running:
mask.sh in ./libs/DCNv2_latest/
For evaluating the results of each model, you can run the corresponding "validate.py".
Also you can run the sequence_test.py for testing your own video sequences.
@inproceedings{zhou2021rta,
title={Revisiting Temporal Alignment for Video Restoration},
author={Kun Zhou and Wenbo Li and Liying Lu and Xiaoguang Han and Jiangbo Lu},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}
year={2022}
}
Our code is for research purposes only.