Awesome
Multi-Scale Memory-Based Video Deblurring
This repository is the Pytorch implementation of Multi-Scale Memory-Based Video Deblurring, appearing in CVPR 2022.
<img src="figures/memdeblur.jpg">Prerequisites
- Python 3.7
- PyTorch 1.9.1
- Torchvision 0.10.1
- pyyaml
- opencv-python
- tensorboard
- tqdm
- lmdb
- scikit-image
- thop
- matplotlib
Checkpoints and Dataset
Please download the checkpoints and dataset from Google Drive.
Inference result on the datasets are also avaliable in the above link.
Our codes use lmdb format. If you would like to use other datasets, please convert them into lmdb format. The details can be found here.
In our CVPR version, the reported GMACs includes a redundant operation. We removed this operation, so the actual GMACs is 325.48.
Training
To train the model on the downsampled GOPRO dataset, run the below command:
python main.py --config config/memdeblur_gopro_ds.yml
To train the model on the original GOPRO dataset, run the below command:
python main.py --config config/memdeblur_gopro_ori.yml
Evaluation
To evaluate the model on the downsampled GOPRO dataset, please turn on the test_only option in the config file and run the below command:
python main.py --config config/memdeblur_gopro_ds.yml
To evaluate the model on the original GOPRO dataset, please turn on the test_only option in the config file and run the below command:
python main.py --config config/memdeblur_gopro_ori.yml
As mentioned in Section 4.1, we reduce the memory size by discarding old memories. This feature is automatically turned on as indicated by test_mem_length: 5 in the config file. To turn this feature off and utilize the full memory, please set it to None, i.e., test_mem_length: ~ .
TODO
We plan to update the evaluation results on DVD dataset in the near future.
Credits
Our codes are modified from ESTRNN and mmediting. We thank for their excellent work!