This paper introduces a new benchmarking dataset for marine snow removal of underwater images. Marine snow is one of the main degradation sources of underwater images that are caused by small particles, e.g., organic matter and sand, between the underwater scene and photosensors. We mathematically model two typical types of marine snow from the observations of real underwater images. The modeled artifacts are synthesized with underwater images to construct large-scale pairs of ground truth and degraded images to calculate objective qualities for marine snow removal and to train a deep neural network. We propose two marine snow removal tasks using the dataset and show the first benchmarking results of marine snow removal. The Marine Snow Removal Benchmarking Dataset is publicly available online.
@article{arxiv.2103.14249,
title = {Marine Snow Removal Benchmarking Dataset},
author = {Reina Kaneko and Yuya Sato and Takumi Ueda and Hiroshi Higashi and Yuichi Tanaka},
journal= {arXiv preprint arXiv:2103.14249},
year = {2024}
}