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Repository for the paper Underwater Image Super-Resolution using Deep Residual Multipliers (ICRA 2020). Pre-print. img1

Resources

  • Proposed dataset: USR-248
  • Proposed model: SRDRM and SRDRM-GAN for underwater image super-resolution
  • Models in comparison: SRGAN, ESRGAN, EDSRGAN, ResNetSR, SRCNN, and DSRCNN
  • Requirements: TensorFlow >= 1.11 and Keras >= 2.2

Usage

  • Download the data, setup data-paths in the training scripts
    • train-GAN-nx.py: SRDRM-GAN, SRGAN, ESRGAN, EDSRGAN
    • train-generative-models-nx.py: SRDRM, ResNetSR, SRCNN, DSRCNN
  • Use the test-scripts for evaluating different models
    • A few test images: data/test/ (ground-truth: high_res)
  • Use the measure.py for quantitative analysis

Bibliography Entry

@inproceedings{islam2020srdrm,
  title={{Underwater Image Super-Resolution using Deep Residual Multipliers}},
  author={Islam, Md Jahidul and Enan, Sadman Sakib and Luo, Peigen and Sattar, Junaed},
  booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
  year={2020},
  organization={IEEE}
}

Acknowledgements

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Underwater Image Super-Resolution using Deep Residual Multipliers. #ICRA2020

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