Gan-based rain noise removal from single-image considering rain composite models

Takuro Matsui, Masaaki Ikehara

研究成果: Conference contribution

抄録

Under severe weather conditions, outdoor images or videos captured by cameras can be affected by heavy rain and fog. In this paper, we address a single-image rain removal problem (de-raining). As compared to video-based methods, single-image based methods are challenging because of the lack of temporal information. Although many existing methods have tackled these challenges, they suffer from overfitting, over-smoothing, and unnatural hue change. To solve these problems, we propose a GAN-based de-raining method. The optimal generator is determined by experimental comparisons. To train the generator, we learn the mapping between rainy and residual images from the training dataset. Besides, we synthesize a variety of rainy images to train our network. In particular, we focus on not only the orientations and scales of rain streaks but also the rainy image composite models. Our method also achieves better performance on both synthetic and real-world images than state-of-the-art methods in terms of quantitative and visual performances.

本文言語English
ホスト出版物のタイトル28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ665-669
ページ数5
ISBN(電子版)9789082797053
DOI
出版ステータスPublished - 2021 1 24
イベント28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
継続期間: 2020 8 242020 8 28

出版物シリーズ

名前European Signal Processing Conference
2021-January
ISSN(印刷版)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
CountryNetherlands
CityAmsterdam
Period20/8/2420/8/28

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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