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Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning

Abdullah Abuolaim           Mhamoud Afifi           Michael S. Brown
York University

teaser gif

Reference github repository for the paper Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning. Abuolaim et al., proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022 (YouTube presentation). If you use our dataset or code, please cite our paper:

@inproceedings{abuolaim2022improving,
  title={Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning},
  author={Abuolaim, Abdullah and Afifi, Mahmoud and Brown, Michael S},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1231--1239},
  year={2022}
}

DLDP Dataset

We collected a new diverse and large Dual-Pixel (DLDP) dataset of 2353 scenes. This dataset consists of 7059 images i.e., 2353 images with their 4706 dual-pixel (DP) sub-aperture views.

  • Dataset

  • Training and testing sets

    • The dataset is divided randomly into:
      • 89% training and 11% testing.
    • Each set has a balanced number of indoor/outdoor scenes and aperture sizes.
    • The 500 scenes of the DPDD dataset [1] are added to the training set.

Our New Image Motion Attribute (NIMAT) Effect

NIMAT effect

Code and Trained Models

Prerequisites

  • 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

Installation

  • Clone with HTTPS this project to your local machine
git clone https://github.com/Abdullah-Abuolaim/multi-task-defocus-deblurring-dual-pixel-nimat.git
cd ./multi-task-defocus-deblurring-dual-pixel-nimat/mdp_code/

Testing

  • Download the final trained model of the second phase used in the main paper i.e., mdp_phase_2_wacv

  • Place the downloaded .hdf5 model inside ModelCheckpoints for testing

  • Download the DPDD dataset [1], or visit project GitHub

  • Run main.py in mdp_code directory as follows:

     python main.py   --op_phase test   --path_to_data $PATH_TO_DPDD_DATA$   --test_model  mdp_phase_2_wacv
    • --training_phase: training phase i.e., phase_1 or phase_2
    • --op_phase: operation phase training or testing
    • --path_to_data: path to the directory that has the DPDD data e.g., ./dd_dp_dataset_canon/
    • --test_model: test model name
    • --downscale_ratio: downscale input test image in case the GPU memory is not sufficient, or use CPU instead
    • The results of the tested models will be saved in the results directory that will be created inside mdp_code

  • The above testing is for evaluating the DPDD dataset quantitively and quantitatively

    • This evaluation is for data that has ground truth
    • This testing is designed based on the directory structure from the DPDD GitHub

Recall that you might need to change

Quick Testing

  • For quick qualitative testing of images in a directory, run the following command: 
     python main.py   --op_phase test_quick   --path_to_data $PATH_TO_DATA$   --test_model  mdp_phase_2_wacv
    • --path_to_data: path to the directory that has the images (no ground truth)
    • --downscale_ratio: downscale input test image in case the GPU memory is not sufficient, or use CPU instead
    • The results of the tested models will be saved in the results directory that will be created inside mdp_code

Recall that you might need to change

Training

  • As described in the main paper, we train our multi-task DP network (MDP) in two phases:
    • First phase: freezing the weights of the deblurring Decoder, then, training with image patches from our new DLDP dataset to optimize for the DP-view synthesis task.
    • Second phase: unfreezing the weights of the deblurring Decoder, then, fine-tuning using images from the DPDD dataset [1] to optimize jointly for both the defocus deblurring and DP-view synthesis tasks.

Phase 1 Training

  • Run main.py in mdp_code directory to start phase 1 training: 
     python main.py   --training_phase  phase_1   --op_phase train   --path_to_data $PATH_TO_DLDP_DATA$
    • --path_to_data: path to the directory that has the DLDP data e.g., ./dldp_data_png/
    • The results of the tested models will be saved in the results directory that will be created inside mdp_code
    • The trained model and checkpoints will be saved in ModelCheckpoints after each epoch
    • A TensorBoard log for each training session will be created at logs to provide the visualization and tooling needed to monitor the training

Recall that you might need to change

Phase 2 Training

  • Download the final trained model of the first phase used in the main paper i.e., mdp_phase_1_wacv
  • Place the downloaded .hdf5 model inside ModelCheckpoints for training
  • You need the first phase trained model to strat phase 2 training
  • Run main.py in mdp_code directory to start phase 2 training: 
     python main.py   --training_phase  phase_2   --op_phase train   --path_to_data $PATH_TO_DPDD_DATA$   --phase_1_checkpoint_model  mdp_phase_1_wacv
    • --path_to_data: path to the directory that has the DPDD data e.g., ./dd_dp_dataset_canon/
    • --phase_1_checkpoint_model: the name of the pretrained model from phase 1 e.g., mdp_phase_1_wacv
    • The results of the tested models will be saved in the results directory that will be created inside mdp_code
    • The trained model and checkpoints will be saved in ModelCheckpoints after each epoch
    • A TensorBoard log for each training session will be created at logs to provide the visualization and tooling needed to monitor the training

Recall that you might need to change

Other Training Options

  • --patch_size: training patch size
  • --img_mini_b: image mini-batch size
  • --epoch: number of training epochs
  • --lr: initial learning rate
  • --schedule_lr_rate: learning rate scheduler (after how many epochs to decrease)
  • --bit_depth: image bit depth datatype, 16 for uint16 or 8 for uint8. Recall that we train with 16-bit images
  • --dropout_rate: the dropout rate of the conv unit at the network bottleneck

NIMAT Effect

  • To generate eight DP-like sub-aperture views (i.e., NIMAT) for each image in a directory, run the following command: 
     python main.py   --op_phase nimat   --test_model  mdp_phase_2_wacv   --path_to_data $PATH_TO_DATA$
    • --path_to_data: path to the directory that has the images (no ground truth)
    • --downscale_ratio: downscale input test image in case the GPU memory is not sufficient, or use CPU instead
    • The results of the tested models will be saved in the results directory that will be created inside mdp_code

Recall that you might need to change

Contact

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

Abdullah Abuolaim (abuolaim@eecs.yorku.ca).

Related Links

  • 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]
  • ICCV'21 paper: Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data   [github]   [presentation]
  • CVPRW'21 paper: NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results   [pdf]   [presentation]
  • WACVW'22 paper: Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels   [pdf]   [presentation]

References

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