Marine Snow Removal in Underwater Images

Bogdan Smolka, Monika Mendrela

2022

Abstract

In this paper two methods of marine snow detection in underwater images are presented. The proposed techniques are based on the pixel corruption measures which enable the identification of clusters forming the marine snow. As the detection of marine snow contaminating the images must be followed by an inpainting step, various techniques which allow to restore the images with missing regions were evaluated. The experiments revealed that the restoration quality of applied inpainting techniques is dependent on the image structure and the size of regions needed to be restored and that their overall efficiency is comparable. Therefore, the faster algorithms should be preferred. To asses the quality of marine snow removal techniques, a database of images with 5 levels of contamination was created. The experiments performed on this database showed that the proposed marine snow detection techniques coupled with fast inpainting methods yield very satisfactory results, superior to the techniques already known from the literature.

Paper Citation

in Harvard Style

Smolka B. and Mendrela M. (2022). Marine Snow Removal in Underwater Images. In Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 1: ROBOVIS; ISBN 978-989-758-611-8, SciTePress, pages 463-471. DOI: 10.5220/0011588200003332

in Bibtex Style

@conference{robovis22,
author={Bogdan Smolka and Monika Mendrela},
title={Marine Snow Removal in Underwater Images},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 1: ROBOVIS},
year={2022},
pages={463-471},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011588200003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 1: ROBOVIS
TI - Marine Snow Removal in Underwater Images
SN - 978-989-758-611-8
AU - Smolka B.
AU - Mendrela M.
PY - 2022
SP - 463
EP - 471
DO - 10.5220/0011588200003332
PB - SciTePress