Recovering Raindrop Removal Images under Heavy Rain

Kosuke Matsumoto, Fumihiko Sakaue, Jun Sato

2020

Abstract

In this paper, we propose a new method for removing raindrops in images under heavy rain. When we drive in heavy rain, the raindrops attached to the windshield form a film and our visibility degrades drastically. In such situations, the existing raindrop removal methods cannot recover clear images, since these methods assume that the background scene is visible through the gap between the raindrops, which does not happen anymore in heavy rain. Thus, we in this paper propose a new method for recovering raindrop removal images under heavy rain from sequential images by using conditional GAN. The results of our experiments on real images and synthetic images show that the proposed method outperforms the state-of-the-art raindrop removal method.

Paper Citation

in Harvard Style

Matsumoto K., Sakaue F. and Sato J. (2020). Recovering Raindrop Removal Images under Heavy Rain. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 128-136. DOI: 10.5220/0009325601280136

in Bibtex Style

@conference{visapp20,
author={Kosuke Matsumoto and Fumihiko Sakaue and Jun Sato},
title={Recovering Raindrop Removal Images under Heavy Rain},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={128-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009325601280136},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Recovering Raindrop Removal Images under Heavy Rain
SN - 978-989-758-402-2
AU - Matsumoto K.
AU - Sakaue F.
AU - Sato J.
PY - 2020
SP - 128
EP - 136
DO - 10.5220/0009325601280136
PB - SciTePress