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Motion Aware Event Representation-driven Image Deblurring-ECCV 2024🫶

This is the official code of Motion Aware Event Representation-driven Image Deblurring. Training Code released!

Motion Aware Event Representation-driven Image Deblurring(MAENet)

Traditional image deblurring struggles with high-quality reconstruction due to limited motion data from single blurred images. Excitingly, the high-temporal resolution of event cameras records motion more precisely in a different modality, transforming image deblurring. However, many event camera-based methods, which only care about the final value of the polarity accumulation, ignore the influence of the absolute intensity change where events generate so fall short in perceiving motion patterns and effectively aiding image reconstruction. To overcome this, in this work, we propose a new event preprocessing technique that accumulates the deviation from the initial moment each time the event is updated. This process can distinguish the order of events to improve the perception of object motion patterns. To complement our proposed event representation, we create a recurrent module designed to meticulously extract motion features across local and global time scales. To further facilitate the event feature and image feature integration, which assists in image reconstruction, we develop a bi-directional feature alignment and fusion module. This module works to lessen inter-modal inconsistencies. Our approach has been thoroughly tested through rigorous experiments carried out on several datasets with different distributions. These trials have delivered promising results, with our method achieving top-tier performance in both quantitative and qualitative assessments.

Overview

This is the overview of our network's architecture: image The differences of our Deviation Accumulation(DA) event representation and other Polarity Accumulation(PA) representation: image image See more details in our paper.

Installation

- pip install -r requirements.txt

Dataset & Data Preprocess

  • Please download the raw GoPro event dataset released by EFNet and follow the README to create the DA event representation.

Evaluation

The quantitative results of our method on GoPro, HS-ERGB and REBlur test datasets. image

Pretrained Model Download

  • TODO

Test

GoPro:

- python basicsr/test.py -opt options/test/GoPro/test_MAENet_GoPro.yml

HS-ERGB:

- python basicsr/test.py -opt options/test/HS_ERGB/test_MAENet_ERGB.yml

REBlur:

- python basicsr/test.py -opt options/test/GoPro/test_MAENet_REBlur.yml

Training

GoPro:

- python basicsr/train.py -opt options/train/GoPro/MAENet_GoPro.yml

HS-ERGB:

- python basicsr/train.py -opt options/train/HS_ERGB/MAENet_ERGB.yml

REBlur:

- python basicsr/train.py -opt options/train/GoPro/MAENet_REBlur.yml

Citation

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

@InProceedings{Sun_2024_ECCV,
    author    = {Sun, Zhijing and Fu, Xueyang and Huang, Longzhuo and Liu, Aiping and Zha, Zheng-Jun},
    title     = {Motion Aware Event Representation-driven Image Deblurring},
    booktitle = {Proceedings of the European conference on computer vision (ECCV)},
    year      = {2024},
    organization = {Springer}
}

Contact

Should you have any question, please contact [email protected]

Acknowledgment: This code is based on the BasicSR toolbox.