Depth-Aware-U-shape-Transformer (DAUT)

Underwater Image Enhancement using Depth Aware U-shape Transformer

The official implementation of (DAUT) depth aware U-shape transformer.

based on u-shape transformer paper, implementation

and uses (DPT) Vision Transformers for Dense Prediction paper, implementation

below visual comparison between DAUT against U-shape Transformer and other physical and non-physical model enhancement methods, enhancing sampled images from our test dataset of different water types. it can be seen DAUT achieves state-of-the-art results.

Setup

install required dependencies from requirement.txt

Training

dataset will be uploaded

Testing

Estimate depth maps for underwater images

1. Download DPT model weights and put it in DPT/weights/
2. Put underwater images in DPT/input/
3. Run python DPT/run_segmentation.py
4. Depth maps will be created in DPT/output_monodepth/
5. Move Depth images “*.png” from DPT/output_monodepth/ to test/depth/

Enhance underwater images

1. Download DAUT model weights and put it in saved_models/G/
2. Put underwater images in test/input/ "along with depth images in test/depth/"
3. Run test.ipynb
4. Enhanced images will be in test/output/

We provided few sample images with their depth for testing in test directory so you can run test.ipynb after downloading DAUT’s weights only