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This repository is an official PyTorch implementation of the paper "Deep Blind Video Super-resolution".
The code is built on Ubuntu 16.04 environment (Python3.6, PyTorch_0.4.1, CUDA8.0, cuDNN5.1) with Tesla V100/1080Ti GPUs.

• ubuntu16.04
• python 3.6(Recommend to use Anaconda)
• pyTorch0.4.1
• numpy
• skimage
• imageio
• matplotlib
• tqdm
• cv2

We use the REDS dataset to train our models. You can download it fromofficial website

We regroup the REDS training and validation sets same asEDVR do:
trainset: the original training (except 4 clips) and validation sets, total 266 clips
validationset: 000, 011, 015 and 020 clips from the original training set, total 4 clips

All the models(X4) can be downloaded fromGoogleDrive.

After download our models in paper, place the foldermodels_in_paper to the path./DBVSRYou can test our super-resolution algorithm with REDS4 dataset. Please organize the testset intestset folder like this:

        |--REDS           |--test              |--HR                  |--000                      |--00000000.png                             :                             :                      |--00000099.png                  |--011                  |--015                  |--020              |--LR                  |--000                      |--00000000.png                             :                             :                      |--00000099.png                  |--011                  |--015                  |--020

please check the data root of test sets in code./code/option/template.py, line 9(args.dir_data_test).(for dbvsr)
please check the data root of test sets in code./code/option/template.py, line 26(args.dir_data_test).(for baseline_lr)
please check the data root of test sets in code./code/option/template.py, line 43(args.dir_data_test).(for baseline_hr)

Then, run the following commands:

cd codepython main.py --test_only

And generated results can be found in./experiment/dbvsr_test/results/ for dbvsr results
And generated results can be found in./experiment/baseline_lr_test/results/ for baseline_lr results
And generated results can be found in./experiment/baeline_hr_test/results/ for baseline_hr results

• To test other benchmarks, you can modify the option(dir_data_test) of the command above.
• To change the save root, you can modify the option(save) of the command above.

If you have downloaded the trainset, please make sure that the trainset has been organized as follows:

       |--REDS           |--train              |--HR                  |--001                      |--00000000.png                      |--00000001.png                             :                             :                      |--00000099.png                  |--002                      :                      :                  |--239              |--LR                  |--001                      |--00000000.png                      |--00000001.png                             :                             :                      |--00000099.png                  |--002                      :                      :                  |--239

Then,please check the data root of train sets in code./code/option/template.py, line 6(args.dir_data).(for dbvsr)
please check the data root of train sets in code./code/option/template.py, line 23(args.dir_data).(for baseline_lr)
please check the data root of train sets in code./code/option/template.py, line 40(args.dir_data).(for baseline_hr)

The command for training is as follow:

cd codepython main.py

The pretrain_model of pwc-net and fcnet can be found in./pretrain.