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(2018) WaterGAN:unsupervised generative network to enable real-time color correction of monocular underwater images

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Li J, Skinner K A, Eustice R M, et al. WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images[J]. IEEE Robotics and Automation Letters, 2018, 3(1): 387-394.


1. Overview

论文

  • 基于SimGAN的思想，提出WaterGAN模型。通过un-supervised方法利用WaterGAN将in-air image渲染成underwater image
  • 基于渲染得到的underwater image，通过supervised方法利用Restoration Network将underwater image恢复成in-air image


  • CNN
  • SimGAN
  • RenderGAN

1.2. 模型

1.2.1. WaterGAN



输入大小：48x64

  • Attenuation


η：wavelength-dependent attenuation coefficient estimated by network
r_c：range from camera to scene
λ：color channel
约束η≥0：确保颜色衰减而不增强

  • Scattering
    • 输入：48x64 depth map + 100 noise (project, reshape, concat)
    • 计算：3个并行CNN
    • 输出：48x64x3 mask


  • Camera Model
    a, b, c, k estimated by network.


约束条件



1.2.2. Restoration Network



使用segNet中的non-parametric upsampling layer (uses the index information from corresponding max-pooling layers).

  • Depth Estimation Network

    • 输入：56x56x3 downsampled
    • 输出：56x56x1
    • Loss：L2

  • Color Restoration Network

    • 输入：480x480x1 upsampled and then padded to 512x512x1
    • 下采样：128x128 AvgPooling
    • Core Component
    • 上采样：512x512 DeConv (initialized by bilinear interpolation)
    • Loss：L2

1.3. 数据集

  • Synthetic
    4 Kinect dataset (B3DO, UW RGB-D Object, NYU Depth, Microsoft 7-scenes). Total 15000 RGB-D images (12000 training, 3000 validation).
  • MHL (University of Michigan’s Marine Hydrodynamics Laboratory)
    7000 underwater images.


  • Port Royal
    6500 underwater images, maximum depth 1.5m.
  • Lizard Island
    6083 underimages, maximum depth 2.0m.

1.4. Metric

  • Color Accuracy
  • Color Consistenct

1.5. 实验结果

  • MHL





  • Validation



  • Skip Connection