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EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment (CVPRW 2021)

Update !!!

• 2022.04.22 🎉🎉🎉 We won the 1st place in NTIRE 2022 BurstSR Challenge again [Paper][Code].
• 2022.01.22 We updated the code to support real track testing and provided the model weights here
• 2021 Now we support 1 GPU training and provide the pretrained model here.

This repository is an official PyTorch implementation of the paper "EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment" from CVPRW 2021, 1st NTIRE21 Burst SR in real track (2nd in synthetic track).

Dependencies

• OS: Ubuntu 18.04
• Python: Python 3.7
• nvidia :
  • cuda: 10.1
  • cudnn: 7.6.1
• Other reference requirements

Quick Start

1.Create a conda virtual environment and activate it

conda create -n pytorch_1.6 python=3.7
source activate pytorch_1.6

2.Install PyTorch and torchvision following the official instructions

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch

3.Install build requirements

pip3 install -r requirements.txt

4.Install apex to use DistributedDataParallel following the Nvidia apex (optional)

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

5.Install DCN

cd DCNv2-pytorch_1.6
python3 setup.py build develop # build
python3 test.py # run examples and check

Training

# Modify the root path of training dataset and model etc.
# The number of GPUs should be more than 1
python main.py --n_GPUs 4 --lr 0.0002 --decay 200-400 --save ebsr --model EBSR --fp16 --lrcn --non_local --n_feats 128 --n_resblocks 8 --n_resgroups 5 --batch_size 16 --burst_size 14 --patch_size 256 --scale 4 --loss 1*L1

Test

# Modify the path of test dataset and the path of the trained model
python test.py --root /data/dataset/ntire21/burstsr/synthetic/syn_burst_val --model EBSR --lrcn --non_local --n_feats 128 --n_resblocks 8 --n_resgroups 5 --burst_size 14 --scale 4 --pre_train ./checkpoints/EBSRbest_epoch.pth

or test on the validation dataset:

python main.py --n_GPUs 1 --test_only --model EBSR --lrcn --non_local --n_feats 128 --n_resblocks 8 --n_resgroups 5 --burst_size 14 --scale 4 --pre_train ./checkpoints/EBSRbest_epoch.pth

Real track evaluation

You may need to download pretrained PWC model to the pwcnet directory (here).

python test_real.py --n_GPUs 1 --model EBSR --lrcn --non_local --n_feats 128 --n_resblocks 8 --n_resgroups 5 --burst_size 14 --scale 4 --pre_train ./checkpoints/BBSR_realbest_epoch.pth --root burstsr_validation_dataset...

Citations

If EBSR helps your research or work, please consider citing EBSR. The following is a BibTeX reference.

@InProceedings{Luo_2021_CVPR,
    author    = {Luo, Ziwei and Yu, Lei and Mo, Xuan and Li, Youwei and Jia, Lanpeng and Fan, Haoqiang and Sun, Jian and Liu, Shuaicheng},
    title     = {EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2021},
    pages     = {471-478}
}

Contact

email: [ziwei.ro@gmail.com, yl_yjsy@163.com]