Implementation of Sea-thru by Derya Akkaynak and Tali Treibitz and made more specific by Keenan Johnson.

Forked from https://github.com/hainh/sea-thru

This fork's only aim is to standardize the dependencies and environment with Docker and Anaconda to make it so that anyone can use it without deep technical knowledge of python.

Additionally, in order to make this an all-in-one package, the Monodepth2 submodule has been cloned and commited directly into this repo, preserving the commit history and original authors.

Docker

1. Run docker compose up --build
   • This can take around 2 hours the first time; from then onwards you only need to run docker compose up to get the container running and it'll boot up instantly.
2. Once the container is built and running, in a separate terminal run docker exec -it seathru /bin/bash
   • Replace seathru in that command if you've modified the SERVICE enviornment variable in .env
3. From within the container you can now run the image processing commands (see Usage section below)
   • This root directory is mounted into the container each time you bring it up via docker compose up, meaning any files you add on your host machine to this directory will also be available within the container.

Process a single underwater image:

python seathru-mono-e2e.py --image ${PATH_TO_IMAGE}

With custom output filename:

python seathru-mono-e2e.py --image input.jpg --output enhanced.png

Process all images in a directory:

python seathru-mono-e2e.py --input-dir ./test_images/input_JPEG --output-dir ./test_images/output_JPEG

Process RAW files (DNG, NEF, CR2, ARW):

python seathru-mono-e2e.py --input-dir ./test_images/input_RAW --output-dir ./test_images/output_RAW --raw

This will:

• Process all JPEG/PNG images in the input directory (or RAW files with --raw flag)
• Save enhanced images to the output directory with "_seathru" suffix
• Automatically create the output directory if it doesn't exist
• Show progress for each image being processed

GPR files require conversion to DNG first (the Docker image includes gpr_tools for this):

# Step 1: Convert GPR to DNG losslessly (inside Docker container)
python gpr_converter.py --input-dir ./test_images/input_GPR --output-dir ./test_images/input_DNG

# Step 2: Process the DNG files
python seathru-mono-e2e.py --input-dir ./test_images/input_DNG --output-dir ./test_images/output_GPR --raw

The Docker image includes gpr_tools (GoPro's official tool) for lossless GPR to DNG conversion.

• --max-size: Limit maximum image dimension (default: no resizing)

  python seathru-mono-e2e.py --image input.jpg --max-size 2000

• --f: Control brightness (default: 2.0)
• --l: Control balance of attenuation constants (default: 0.5)
• --p: Control locality of illuminant map (default: 0.01)
• --output-graphs: Generate debug visualization graphs
• --no-cuda: Force CPU processing if CUDA is unavailable

A recent advance in underwater imaging is the Sea-Thru method, which uses a physical model of light attenuation to reconstruct the colors in an underwater scene. This method utilizes a known range map to estimate backscatter and wideband attenuation coefficients. This range map is generated using structure-from-motion (SFM), which requires multiple underwater images from various perspectives and long processing time. In addition, SFM gives very accurate results, which are generally not required for this method. In this work, we implement and extend Sea-Thru to take advantage of convolutional monocular depth estimation methods, specifically the Monodepth2 network. We obtain satisfactory results with the lower-quality depth estimates with some color inconsistencies using only one image.