L^2UWE: Low-light underwater image enhancement
This repo contains the implementation of our work: L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion, by Tunai Porto Marques and Alexandra Branzan Albu (presented at the 2020 CVPR Workshop NTIRE: New Trends in Image Restoration and Enhancement held in Seattle, June 15th).
L^2UWE uses a single image, local contrast information and a multi-scale fusion process to highlight visual features from the input that might have been originally hidden because of low-light settings.
If L^2UWE proves to be useful to your work, we ask that you cite its related publications:
BibTeX
@inProceedings{Marques_2020_CVPR_Workshops,
title={L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion},
author={Porto Marques, Tunai and Branzan Albu, Alexandra},
booktitle={The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year={2020}}@article{porto2019contrast,
title={A Contrast-Guided Approach for the Enhancement of Low-Lighting Underwater Images},
author={Porto Marques, Tunai and Branzan Albu, Alexandra and Hoeberechts, Maia},
journal={Journal of Imaging},
volume={5},
number={10},
pages={79},
year={2019},
publisher={Multidisciplinary Digital Publishing Institute} }
System requirements
- MATLAB
- Image Processing Toolbox
The framework was tested on MATLAB versions R2019b and R2020a.
Demo script
Open the "demo.m" script and point to your input image in the "imread" command. Some sample low-lighting underwater and aerial images are already provided in the "./data/" folder.
Once processed, the partial and final results are saved on the "./out/" folder.
Dataset
The dataset used in the article, OceanDark, can be accessed at OceanDark.
Repo author
Tunai Porto Marques (tunaip@uvic.ca), website