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Fast underwater image enhancement for Improved Visual Perception. #TensorFlow #PyTorch

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TensorFlow and PyTorch implementations of the paper Fast Underwater Image Enhancement for Improved Visual Perception (RA-L 2020) and other GAN-based models.

funie-fig

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Enhanced underwater imagery Improved detection and pose estimation
det-enh det-gif

FUnIE-GAN Features

  • Provides competitive performance for underwater image enhancement
  • Offers real-time inference on single-board computers
    • 48+ FPS on Jetson AGX Xavier, 25+ FPS on Jetson TX2
    • 148+ FPS on Nvidia GTX 1080
  • Suitable for underwater robotic deployments for enhanced vision

FUnIE-GAN Pointers

Underwater Image Enhancement: Recent Research and Resources

2019

Paper Theme Code Data
Multiscale Dense-GAN Residual multiscale dense block as generator
Fusion-GAN FGAN-based model, loss function formulation U45
UDAE U-Net denoising autoencoder
VDSR ResNet-based model, loss function formulation
JWCDN Joint wavelength compensation and dehazing
AWMD-Cycle-GAN Adaptive weighting for multi-discriminator training
WAug Encoder-Decoder Encoder-decoder module with wavelet pooling and unpooling GitHub
Water-Net Dataset and benchmark GitHub UIEB

2017-18

Paper Theme Code Data
UGAN Several GAN-based models, dataset formulation GitHub Uw-imagenet
Underwater-GAN Loss function formulation, cGAN-based model
LAB-MSR Multi-scale Retinex-based framework
Water-GAN Data generation from in-air image and depth pairings GitHub MHL, Field data
UIE-Net CNN-based model for color correction and haze removal

Non-deep Models

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