Linker6610 / recurrent-defocus-deblurring-synth-dual-pixel

Reference github repository for the paper "Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data". We propose a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Leveraging these realistic synthetic DP images, we introduce a new recurrent convolutional network (RCN) architecture that can improve defocus deblurring results and is suitable for use with single-frame and multi-frame data captured by DP sensors.

Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data

Abdullah Abuolaim¹     Mauricio Delbracio²     Damien Kelly²     Michael S. Brown¹     Peyman Milanfar^2
1York University         ²Google Research

Reference github repository for the paper Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data. Abuolaim et al., arXiv:2012.03255, 2020. If you use our dataset or code, please cite our paper:

@article{abuolaim2020synthdp,
  title={Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data},
  author={Abuolaim, Abdullah and Delbracio, Mauricio and Kelly, Damien and Brown, Michael S and Milanfar, Peyman},
  booktitle={arXiv:2012.03255},
  year={2020}
}

Dataset, code, and trained models

Will be available soon.

Contact

Should you have any question/suggestion, please feel free to reach out:

Abdullah Abuolaim ([e-mail])

Related Links

• ECCV'18 paper: Revisiting Autofocus for Smartphone Cameras   [project page]
• WACV'20 paper: Online Lens Motion Smoothing for Video Autofocus   [project page]
• ICCP'20 paper: Modeling Defocus-Disparity in Dual-Pixel Sensors   [github]
• ECCV'20 paper: Defocus Deblurring Using Dual-Pixel Data   [project page]   [github]

References

[1] Abdullah Abuolaim and Michael S. Brown. Defocus Deblurring Using Dual-Pixel Data. In ECCV, 2020.