TensorFlow and PyTorch implementations of the paper Fast Underwater Image Enhancement for Improved Visual Perception (RA-L 2020) and other GAN-based models.
Resources
- Training pipelines for FUnIE-GAN and UGAN (original repo) on TensorFlow (Keras) and PyTorch
- Modules for image quality analysis based on UIQM, SSIM, and PSNR (see Evaluation)
| Enhanced underwater imagery | Improved detection and pose estimation |
|---|---|
 |
 |
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
- Paper: https://ieeexplore.ieee.org/document/9001231
- Preprint: https://arxiv.org/pdf/1903.09766.pdf
- Datasets: http://irvlab.cs.umn.edu/resources/euvp-dataset
- Bibliography entry for citation:
@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} }
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
- Sea-Thru (project page)
- Haze-line-aware Color Restoration (code)
- Local Color Mapping Combined with Color Transfer (code)
- Real-time Model-based Image Color Correction for Underwater Robots (code)
- All-In-One Underwater Image Enhancement using Domain-Adversarial Learning (code)
- Difference Backtracking Deblurring Method for Underwater Images
- Guided Trigonometric Bilateral Filter and Fast Automatic Color correction
- Red-channel Underwater Image Restoration (code)
Reviews, Metrics, and Benchmarks
- Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
- Human-Visual-System-Inspired Underwater Image Quality Measures
- An Underwater Image Enhancement Benchmark Dataset and Beyond
- An Experimental-based Review of Image Enhancement and Restoration Methods (code)
- Diving Deeper into Underwater Image Enhancement: A Survey
- A Revised Underwater Image Formation Model