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., proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021 (YouTube presentation). If you use our dataset or code, please cite our paper:

@article{abuolaim2021learning,
  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={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}

• The code tested with:

  • Python 3.8.3
  • Numpy 1.19.1
  • Scipy 1.5.2
  • Wand 0.6.3
  • Imageio 2.9.0
  • OpenCV 4.4.0

  Despite not tested, the code may work with library versions other than the specified

• Clone with HTTPS this project to your local machine

git clone https://github.com/Abdullah-Abuolaim/recurrent-defocus-deblurring-synth-dual-pixel.git
cd ./recurrent-defocus-deblurring-synth-dual-pixel/synthetic_dp_defocus_blur_code/

• Download SYNTHIA-SF dataset or visit SYNTHIA downloads

  • SYNTHIA-SF dataset contains six image sequences: SEQ1-SEQ6
  • Traning: SEQ1, SEQ2, SEQ4, SEQ5, SEQ6
  • Testing: SEQ3

• Run the code in synthetic_dp_defocus_blur_code directory to start generating data as follows:

   python synthetic_dp_defocus_blur.py --data_dir ./SYNTHIA-SF/ --radial_dis True

  • --data_dir: path to the downloaded SYNTHIA-SF directory
  • --radial_dis: to apply radial distortion on the generated DP views

• Running above will create the generated dataset dd_dp_dataset_synth_vid in synthetic_dp_defocus_blur_code

  • Generate synthetic image sequence for each camera set (i.e., the five camera sets defined in the main paper)
  • There will be 30 image sequences generated in total (5 camera sets x 6 image sequences)

• The generated dataset is organized based on the following directory structure

  • $dir_name$_c: directory of the final output combined images
  • $dir_name$_l: directory of the corresponding DP left view images
  • $dir_name$_r: directory of the corresponding DP right view images
  • source: images exhibiting defocus blur
  • target: the corresponding all-in-focus images
  • seq_n: image sequence number

• The code tested with:

  • Python 3.8.3
  • TensorFlow 2.2.0
  • Keras 2.4.3
  • Numpy 1.19.1
  • Scipy 1.5.2
  • Scikit-image 0.16.2
  • Scikit-learn 0.23.2
  • OpenCV 4.4.0

  Despite not tested, the code may work with library versions other than the specified

• Clone with HTTPS this project to your local machine

git clone https://github.com/Abdullah-Abuolaim/recurrent-defocus-deblurring-synth-dual-pixel.git
cd ./recurrent-defocus-deblurring-synth-dual-pixel/rdpd_code/

• All the trained models used in the main paper and supplemental material can be downloaded link

• Place the downloaded .hdf5 model inside ModelCheckpoints for testing

• Download DP defocus deblurring dataset [1] link, or visit project GitHub link

• After downloading and generating datasets, place them in the same directory e.g., dd_dp_datasets

• Run main.py in rdpd_code directory as follows:

   python main.py --op_phase test --test_model RDPD+ --data_dir ./dd_dp_datasets/

  • --op_phase: operation phase training or testing
  • --test_model: test model name
  • --data_dir: path to the directory that has both datasets i.e., dd_dp_dataset_canon and dd_dp_dataset_synth_vid

• The results of the tested models will be saved in results directory that will be created inside rdpd_code

Recall that you might need to change

• Download DP defocus deblurring dataset [1] link, or visit project GitHub link

• After downloading and generating datasets, place them in the same directory e.g., dd_dp_datasets

• Run main.py in rdpd_code directory as follows:

   python main.py --op_phase train --ms_edge_loss True --data_dir ./dd_dp_datasets/

  • --op_phase: operation phase training or testing
  • --ms_edge_loss: whether to use our edge loss or not in addition to the typical MSE loss
  • --data_dir: path to the directory that has both datasets i.e., dd_dp_dataset_canon and dd_dp_dataset_synth_vid

• Other training options

  • --ms_edge_loss_weight_x: the weight of our edge loss at the vertical direction
  • --ms_edge_loss_weight_y: the weight of our edge loss at the horizontal direction
  • --patch_size: training patch size
  • --img_mini_b: image mini-batch size
  • --vid_mini_b: video mini-batch size
  • --num_frames: number of video frames
  • --epoch: number of training epochs
  • --lr: initial learning rate
  • --schedule_lr_rate': learning rate scheduler (after how many epochs to decrease)
  • --dropout_rate': dropout rate of the convLSTM unit

• The trained model and checkpoints will be saved in ModelCheckpoints after each epoch

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

Abdullah Abuolaim (abuolaim@eecs.yorku.ca)

• ECCV'18 paper: Revisiting Autofocus for Smartphone Cameras   [project page]
• WACV'20 paper: Online Lens Motion Smoothing for Video Autofocus   [project page]   [presentation]
• ICCP'20 paper: Modeling Defocus-Disparity in Dual-Pixel Sensors   [github]   [presentation]
• ECCV'20 paper: Defocus Deblurring Using Dual-Pixel Data   [project page]   [github]   [presentation]
• CVPRW'21 paper: NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results   [pdf]   [presentation]
• WACV'22 paper: Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning   [github]   [presentation]
• WACVW'22 paper: Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels   [pdf]   [presentation]

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