Deep-Sea ROV Image Restoration

This repository contains code to restore images and videos taken by deep-sea ROVs (Remotely Operated Vehicles) at depths without natural light (most below a depth of 2000m). Specifically, it focuses on images and videos from the Norwegian Petroleum Directorate (NPD) and University of Bergen (UiB) research cruises at the mid-ocean ridges of Mohn's Ridge and Knipovich Ridge.
The restoration process aims to provide a better starting point for both visual human interpretation and machine object detection. For videos, each frame is processed individually.

Dataset

The ROV data from NPD/UiB data is publicly available. For more information visit the NPD-website.
This repository includes a few test images and short video clips for demonstration purposes:

• 2019 HD images and video (1920x1080p).
• 2018 SD images and video (704x576p).

Image restoration model

The image restoration steps include:

#	Technique	Description
1	Noise Reduction	Bilateral filter-based smoothing
2	Dehazing	Uses Underwater Dark Channel Prior (UDCP) to reduce scattering and absorption
3	White Balancing	Restores color balance using the "Gray World Assumption" method
4	Contrast Enhancement	Uses CLAHE* on LAB for contrast without altering color balance or hue
5	Color Correction	Uses CLAHE* on RGB to adjust contrast with potential color balance/hue changes
6	Edge Enhancement	Employs Canny edge detection
7	Sharpening	Enhances details using Unsharp Mask

(*) CLAHE stands for Contrast Limited Adaptive Histogram Equalization and is a commonly used method for contrast enhancement.

Repository Structure

The repository contains:

Toolbox Scripts:

• plot_toolbox.py: Contains functions related to plotting and inspecting the results.
• restoration_toolbox.py: Contains functions for image restoration.

Jupyter Notebooks:

• process_images.ipynb: Notebook to process images.
• process_videos.ipynb: Notebook to process videos.
• concatenate_videos.ipynb: Notebook to concatenate videos. For comparison between original vs. processed videos.

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Demo images

Original images are on the left and processed images are on the right.
These images were processed with a relatively mild/safe setting to be suitable for a wide variety of scenes.

Note: If GitHub isn't rendering the images, they can be seen or downloaded from the folders:

• 2019/HD/test-images
• 2019/HD/result-images

Demo video

A demo video showing a side-by-side comparison of the original and processed videos can be seen or downloaded here. The in-browser player has a resolution limit of 720p, so download is preferred for better quality.

• Video_comparison_of_Original_and_Processed

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Getting Started

Environment Setup using Conda

1. Create the conda environment:

   conda create -n restoration_env

2. Activate the created environment:

   conda activate restoration_env

3. Install the required packages:

   conda install numpy matplotlib opencv

If you need specific versions of the packages, they are:

numpy==1.25.0
matplotlib==3.7.1
opencv==4.8.0.74

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Further work

• Make model dynamically fine-tune parameters settings for each image based on image statistics.
  The current model runs 1 parameter setting (a compromise) for all images in the folder.

Contribution

Feel free to contribute, raise issues or suggest enhancements!