Boosted Video Super Resolution

"Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization" by Yuhao Huang, Hang Dong, Jinshan Pan, Chao Zhu, Yu Guo, Ding Liu, Lean Fu, Fei Wang

Dependencies

Boosted BasicVSR and Boosted EDVR are same as the BasicVSR and EDVR, respectively.

Test

1. Download the Pretrained model and Test set.

2. Run the test_basicvsr.py or test_edvr.py with cuda on command line:

$python test_basicvsr.py --model Boosted_BasicVSR --resume /data/models/basicvsr_reds4.pth --dataset_test /data/DTVIT-test --save_path /data/DTVIT_result --gpu_ids 0

$python test_edvr.py --model Boosted_EDVR --resume_1f /data/models/edvr_1f_reds4.pth --resume_3f /data/models/edvr_3f_reds4.pth --resume_5f /data/models/edvr_5f_reds4.pth --dataset_test /data/DTVIT-test --save_path /data/DTVIT_result --gpu_ids 0

Diverse Types Videos with Irregular Trajectories Dataset(DTVIT)

We have collected a new DTVIT dataset with more scenes which contain stationary objects and background, including live streaming, TV program, sports live, movie and television, surveillance camera, advertisement and first-person videos with irregular trajectories.

Citation

If you use these models in your research, please cite:

@misc{boosted_vsr,
Author = {Yuhao Huang and Hang Dong and Jinshan Pan and Chao Zhu and Yu Guo and Ding Liu and Lean Fu and Fei Wang},
Title = {Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization},
Year = {2022},
Eprint = {arXiv:2207.08674},
}