Google Colab demo:

• This paper deals with the underwater image restoration.
• For this, we have considered two of the main low-level vision tasks,
  • image enhancement, and
  • super-resolution.
• For underwater image enhancement (uie), we have utilized publicly available datasets
  • EUVP, and
  • UIEB.
• For super-resolution, we have used UFO-120 dataset.
• Below, we provide the detailed instructions for each task in a single README file to reproduce the original results.

arXiv version

1. Results
2. Prerequisites
3. Datasets Preparation
4. Usage
5. Evaluation Metrics
6. Processing underwater degraded videos
7. For Underwater Semantic Segmentation and 2D pose Estimation Results
8. License and Citations
9. Send us feedback
10. Acknowledgement
11. Future Releases

Also, the codes work with minimum requirements as given below.

# tested with the following dependencies on Ubuntu 16.04 LTS system:
Python 3.5.2
Pytorch '1.0.1.post2'
torchvision 0.2.2
opencv 4.0.0
scipy 1.2.1
numpy 1.16.2
tqdm

To install using linux env

pip install -r requirements.txt

cd uie_euvp

• Download the test_samples from EUVP dataset in the current directory (preferably). -If you have downloaded somewhere else on the machine, set the absolute path of the test-set arguments in options.py

---testing_dir_inp
---testing_dir_gt

cd uie_uieb

• Download the UIEB and Challenging-60 datasets in the current directory (preferably).
• If you have downloaded somewhere else on the machine, set the absolute path of the test-set arguments in options.py

---testing_dir_inp
---testing_dir_gt

• The UIEB test set used in our paper is available at Google drive.
• Set the above arguments for this too if utilized.
• Note, the challenging-60 set may not have ground truth clean images. So for that, you can leave the argument --testing_dir_gt blank.
• Ours results on challenging-60 set are available at Google drive.

cd super-resolution

• For super-resolution, we have provided separate code files for following configs: 2X, 3X, and 4X.
• To test on 2X, use the following steps:

cd 2X

• Download the UFO-120 test-set in the current directory (preferably).
• If you have already downloaded somewhere else on the machine, set the absolute path of the test-set arguments in options.py

---testing_dir_inp
---testing_dir_gt

• lrd consists of lower-resolution images, whereas hr consists of corresponding high-resolution images.
• Repeat the above steps for remaining SR configs: 3X, and 4X.

• Similar to test-sets, define training-set folders absolute path in the respective options.py file for each of the datasets above.

• Once the test-sets as described above are set.
• You can test a model for a given task using the following command:

export CUDA_VISIBLE_DEVICES=0 #[optional]
python test.py  

• Results will be saved in facades folder of the pwd.

• Once the training-sets as described above are set.
• You can train a model for a given task using the following command:

export CUDA_VISIBLE_DEVICES=0 #[optional]
python train.py --checkpoints_dir --batch_size --learning_rate             

• Models will be saved in --checkpoints_dir with naming convention netG_[epoch].pt.
• Demo codes for plotting loss curve during training are provided in utils/loss folder.

• Image quality metrics (IQMs) used in this work are provided with both Python and Matlab.
• Thanks to Funie-GAN for providing the python implementations of IQMs.
• Sample usage of IQMs in python implementation is provided at Line.

  ### compute SSIM and PSNR
  SSIM_measures, PSNR_measures = SSIMs_PSNRs(CLEAN_DIR, result_dir)
  print("SSIM on {0} samples".format(len(SSIM_measures))+"\n")
  print("Mean: {0} std: {1}".format(np.mean(SSIM_measures), np.std(SSIM_measures))+"\n")
  print("PSNR on {0} samples".format(len(PSNR_measures))+"\n")
  print("Mean: {0} std: {1}".format(np.mean(PSNR_measures), np.std(PSNR_measures))+"\n")
  measure_UIQMs(result_dir)

• Use the same for EUVP and UFO-120.
• For UIEB, we have utlized matlab implementations. Before running ssim_psnr.m, set clean path at Line1, Line2 and result path at Line.
• Below are the results you should get for EUVP dataset

• Below are the results you should get for UIEB dataset

• Below are the results you should get for UFO-120 dataset

• We have also provided the sample codes for processing the degraded underwater videos.
• To run, use the following steps:

cd uw_video_processing

• Download the degraded underwater video in pwd.
• To demonstrate an e.g., we have used a copyright-free sample degraded video, which is provided as degraded_video.mp4 in pwd.
• To test custom degraded video, replace the video path at line.
• For the output enhanced video path, set line.
• To run, execute

python test.py

• To generate segmentation maps on enhanced images, follow SUIM.
  • Post-processing codes related to this are available in utils folder.
• To generate 2D pose of the human divers in an enhanced underwater image, follow OpenPose.

• The usage of this software is only for academic purposes. One can not use it for commercial products in any form.
• If you use this work or codes (for academic purposes only), please cite the following:

@misc{sharma2021wavelengthbased,
      title={Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration}, 
      author={Prasen Kumar Sharma and Ira Bisht and Arijit Sur},
      year={2021},
      eprint={2106.07910},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

 @article{islam2019fast,
     title={Fast Underwater Image Enhancement for Improved Visual Perception},
     author={Islam, Md Jahidul and Xia, Youya and Sattar, Junaed},
     journal={IEEE Robotics and Automation Letters (RA-L)},
     volume={5},
     number={2},
     pages={3227--3234},
     year={2020},
     publisher={IEEE}
}

@ARTICLE{8917818,  
    author={Li, Chongyi 
            and Guo, Chunle 
            and Ren, Wenqi 
            and Cong, Runmin 
            and Hou, Junhui 
            and Kwong, Sam 
            and Tao, Dacheng},  
    journal={IEEE Transactions on Image Processing},   
    title={An Underwater Image Enhancement Benchmark Dataset and Beyond},   
    year={2020},  
    volume={29},  
    number={},  
    pages={4376-4389},  
    doi={10.1109/TIP.2019.2955241}
}

@inproceedings{eriba2019kornia,
  author    = {E. Riba, D. Mishkin, D. Ponsa, E. Rublee and G. Bradski},
  title     = {Kornia: an Open Source Differentiable Computer Vision Library for PyTorch},
  booktitle = {Winter Conference on Applications of Computer Vision},
  year      = {2020},
  url       = {https://arxiv.org/pdf/1910.02190.pdf}
}

@inproceedings{islam2020suim,
  title={{Semantic Segmentation of Underwater Imagery: Dataset and Benchmark}},
  author={Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz, 
              Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  organization={IEEE/RSJ}
}

@article{8765346,
  author = {Z. {Cao} and G. {Hidalgo Martinez} and T. {Simon} and S. {Wei} and Y. A. {Sheikh}},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title = {OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  year = {2019}
}

• If you have any queries or feedback, please contact us @(kumar176101005@iitg.ac.in or ibisht@iitg.ac.in).

• For computing resources, we acknowledge the Department of Biotechnology, Govt. of India for the financial support for the project BT/COE/34/SP28408/2018.
• Some portion of the code are adapted from FUnIE-GAN. The authors greatfully acknowledge it!
• We acknowledge the support of publicly available datasets EUVP, UIEB, and UFO-120.

• We are in the process of releasing web-app soon. [Done]
• More flexible training modules using visdom to be added soon.