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. Presentation videos: 1 min. version 10 min. version
If L^2UWE proves to be useful to your work, we ask that you cite its related publications:
@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={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
pages={538-539},
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} }
- MATLAB
- Image Processing Toolbox
The framework was tested on MATLAB versions R2019b and R2020a.
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.
The dataset used in the development of L^2UWE, OceanDark, can be found in this repo at data/OceanDark2_0. Detailed information about it can be found at OceanDark.
Tunai Porto Marques (tunaip@uvic.ca), website