Event-based motion deblurring has shown promising results by exploiting low-latency events. However, current approaches are limited in their practical usage, as they assume the same spatial resolution of inputs and specific blurriness distributions. This work addresses these limitations and aims to generalize the performance of event-based deblurring in real-world scenarios. We propose a scale-aware network that allows flexible input spatial scales and enables learning from different temporal scales of motion blur. A two-stage self-supervised learning scheme is then developed to fit real-world data distribution. By utilizing the relativity of blurriness, our approach efficiently ensures the restored brightness and structure of latent images and further generalizes deblurring performance to handle varying spatial and temporal scales of motion blur in a self-distillation manner. Our method is extensively evaluated, demonstrating remarkable performance, and we also introduce a real-world dataset consisting of multi-scale blurry frames and events to facilitate research in event-based deblurring.

• Python 3.7
• NVIDIA GPU + CUDA
• Pytorch-Lightning 1.6.0
• numpy, argparse, yaml, opencv-python

You can create a new Anaconda environment as follows.

conda create -n gem python=3.7
conda activate gem

Clone this repository.

git clone [email protected]:XiangZ-0/GEM.git

Install the above dependencies and Deformable Convolution V2

cd GEM
pip install -r requirements.txt
cd codes/model/DCN_v2/
sh make.sh

Pretrained models and datasets can be downloaded via One Drive.
In our paper, we conduct experiments on three types of data:

• Ev-REDS contains synthetic 1280x640 blurry images and synthetic 320x160 events. We first convert REDS into high frame rate videos using RIFE, and then obtain blurry images by averaging sharp frames and generate events from down-sampled images via VID2E.
• HS-ERGB contains synthetic blurry images and real-world events from HS-ERGB. We first convert HS-ERGB into high frame rate videos using Time Lens and then synthesize blurry images by averaging sharp frames. Since only the test set of HS-ERGB is available, we choose 4 sequences (far-bridge_lake_01, close-fountain_schaffhauserplatz_02, close-spinning_umbrella, and close-water_bomb_floor_01) for testing and leave the rest for training. We mannually filter the static frames in the HS-ERGB dataset (where no motion blur occurs) to ensure valid evaluation of deblurring performance.
• MS-RBD contains real-world blurry images and real-world events collected by ourselves. A beam splitter connecting a FLIR BlackFly S RGB camera and a DAVIS346 event camera is built for data collection. In total, our MS-RBD contains 32 sequences composed of 22 indoor and 10 outdoor scenes, where each sequence consists of 60 RGB 1152x768 blurry frames and the concurrent 288x192 events. For self-supervised methods, we select 5 and 3 sequences from the indoor and outdoor scenes for testing and leave the rest for training. For supervised approaches, all sequences can be used for qualitative evaluation of deblurring performance in real-world scenarios.

• Change the parent directory to ./codes/

cd codes

• Download and unzip pretrained models to directory ./checkpoint/
• Download and unzip datasets to directory ./datasets/

• Test on Ev-REDS data

python main.py --yaml_path configs/evreds_test.yaml

• Test on HS-ERGB data

python main.py --yaml_path configs/hsergb_test.yaml

• Test on MS-RBD data

python main.py --yaml_path configs/msrbd_test.yaml

Deblurred results will be saved in ./results/. Note that the script will automatically compute PSNR and SSIM for Ev-REDS and HS-ERGB datasets. Since MS-RBD is a real-world dataset without ground-truth images, we predict the central sharp latent image for qualitative evaluation in real-world scenarios.

For testing on your own datasets, we recommend packing your data in the MS-RBD format and then modifying the following parameters in configs/msrbd_test.yaml according to your needs.

- load_dir:        # change it to your path to load checkpoints
- root_path:       # change it to your dataset directory
- save_path:       # change it to your result directory
- scale_factor:    # change it according to the spatial resolution ratio of images over events in your dataset

Then it is good to go.

• Train on Ev-REDS data

python main.py --yaml_path configs/evreds_train.yaml

• Train on HS-ERGB data

python main.py --yaml_path configs/hsergb_train.yaml

• Train on MS-RBD data

python main.py --yaml_path configs/msrbd_train.yaml

If you want to train a model on your own dataset (especially real-world datasets), it is recommended to pack your data in the MS-RBD format and then modify configs/msrbd_train.yaml according to your needs for training. :)

If you find our work useful in your research, please consider citing:

@inproceedings{zhang2023generalizing,
  title={Generalizing Event-Based Motion Deblurring in Real-World Scenarios},
  author={Zhang, Xiang and Yu, Lei and Yang, Wen and Liu, Jianzhuang and Xia, Gui-Song},
  year={2023},
  booktitle={ICCV},
}

This code is built based on the Pytorch Lightning template, LIIF, and Deformable Convolution V2.