- Implementations of FUnIE-GAN for underwater image enhancement
- Simplified implementations of UGAN and its variants (original repo)
- Modules for quantifying image quality base on UIQM, SSIM, and PSNR
- Implementation: TensorFlow >= 1.11.0, Keras >= 2.2, and Python 2.7
| Perceptual enhancement | Color and sharpness | Hue and contrast |
|---|---|---|
 |
 |
 |
| Enhanced underwater imagery | Improved detection and pose estimation |
|---|---|
 |
 |
- 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
- Paper: 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={To appear at the IEEE Robotics and Automation Letters (RA-L)}, year={2020} }
- Download the data, setup data-paths in the training-scripts
- Use paired training for FUnIE-GAN or UGAN, and unpaired training for FUnIE-GAN-up
- Checkpoints: checkpoints/model-name/dataset-name
- Samples: data/samples/model-name/dataset-name
- Use the test-scripts for evaluating different models
- A few test images: data/test/A (ground-truth: GTr_A), data/test/random (unpaired)
- Output: data/output
- Use the measure.py for quantitative analysis based on UIQM, SSIM, and PSNR
- A few saved models are provided in saved_models/
- Issues with unpaired training (as discussed in the paper)
- Inconsistent coloring, inaccurate modeling of sunlight
- Often poor hue rectification (dominant blue/green hue)
- Hard to achieve training stability
| 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 | UIEBD |
| 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 |
- 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)
- 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
- https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
- https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras
- https://github.com/cameronfabbri/Underwater-Color-Correction
- https://github.com/eriklindernoren/Keras-GAN
- https://github.com/phillipi/pix2pix
- https://github.com/wandb/superres
- https://github.com/aiff22/DPED
- https://github.com/roatienza/Deep-Learning-Experiments
- https://github.com/CMU-Perceptual-Computing-Lab/openpose
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