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Underwater Image Super-Resolution using Deep Residual Multipliers (SRDRM) on USR-248 data

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Resources

  • Implementations of SRDRM and SRDRM-GAN for underwater image super-resolution
  • Simplified implementation of SRGAN, ESRGAN, EDSRGAN, ResNetSR, SRCNN, and DSRCNN
  • Implementation: TensorFlow >= 1.11.0, Keras >= 2.2, and Python 2.7
Single Image Super-Resolution (SISR) Color and sharpness
det-1a det-1b

Pointers

2x SISR performance 4x SISR performance
det-enh det-gif

Usage

  • Download the data, setup data-paths in the training scripts
  • Use the individual scripts for training 2x, 4x, 8x SISR models
    • train-GAN-nx.py: SRDRM-GAN, SRGAN, ESRGAN, EDSRGAN
    • train-generative-models-nx.py: SRDRM, ResNetSR, SRCNN, DSRCNN
    • Checkpoints: checkpoints/dataset-name/model-name/
    • Samples: images/dataset-name/model-name/
  • Use the test-scripts for evaluating different models
    • A few test images: data/test/ (ground-truth: high_res)
    • Output: data/output/
  • A few saved models are provided in checkpoints/saved/
  • Use the measure.py for quantitative analysis based on UIQM, SSIM, and PSNR

Constraints and Challenges

  • Trade-offs between performance and running time. Requirements:
    • Running time >= 5 FPS on Jetson-TX2
    • Model size <= 12MB (no quantization)
  • Challenges
    • Performance for 8x models
    • Inconsistent coloring, infrequent noisy patches

Acknowledgements

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Underwater Image Super-Resolution using Deep Residual Multipliers (SRDRM) on USR-248 data

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