RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution [PRCV2023]

Official implementation of RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution

Performance and Runtime Comparison

Performance and runtime comparison on SyntheticBurst dataset.

Prerequisites

python 3.8.5, pytorch 1.12, mmcv-full 1.7.1
OpenCV, NumPy, Pillow, tqdm, scikit-image and tensorboardX.

Datasets

SntheticBurst training dataset

Download the Zurich RAW to RGB canon set from here and unpack the zip file.

SyntheticBurst testing dataset

Downloaded the dataset here and unpack the zip file.

real-world BurstSR traing and testing dataset

The train and test dataset can be downloaded and unpacked using the util_scripts/download_burstsr_dataset.py script.

Quick Start

Pre-trained models

we provide the pre-trained models in the [./pretrained_networks] folder.

Test on SyntheticBurst Dataset

You can test RBSR on SyntheticBurst dataset by running

python ./run_test_syn.py 

Please change the the synburstval_dir variable in admin/local.py to your test dataset path.

Test on BurstSR Dataset

You can test RBSR on BurstSR dataset by running

python ./run_test_real.py 

Please change the the burstsr_dir variable in admin/local.py to your test dataset path.

Train on SyntheticBurst Dataset

You can train RBSR on SyntheticBurst dataset by running

python ./run_training.py dbsr RBSR_synthetic

Please change the the zurichraw2rgb_dir variable in admin/local.py to your train dataset path.

Train on BurstSR Dataset

You can train RBSR on BurstSR dataset by running

python ./run_training.py dbsr RBSR_realworld

Please change the the burstsr_dir variable in admin/local.py to your train dataset path.

Acknowledgement

• This implementation is based ondeep-burst-sr.
• We borrow some codes from mmagic, which is an open-source image and video editing toolbox.