Single-image rain removal using residual deep learning

Takuro Matsui, Takanori Fujisawa, Takuro Yamaguchi, Masaaki Ikehara

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Most outdoor vision systems can be influenced by rainy weather conditions. In this paper, we address a rain removal problem from a single image. Some existing de-raining methods suffer from hue change due to neglect of the information in low frequency layer. Others fail in assuming enough rainy image models. To solve them, we propose a residual deep network architecture called ResDerainNet. Based on the deep convolutional neural network (CNN), we learn the mapping relationship between rainy and residual images from data. Furthermore, for training, we synthesize rainy images considering various rain models. Specifically, we mainly focus on the composite models as well as orientations and scales of rain streaks. The experiments demonstrate that our proposed model is applicable to a variety of images. Compared with state-of-the-art methods, our proposed method achieves better results on both synthetic and real-world images.

本文言語English
ホスト出版物のタイトル2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
出版社IEEE Computer Society
ページ3928-3932
ページ数5
ISBN(電子版)9781479970612
DOI
出版ステータスPublished - 2018 8月 29
イベント25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
継続期間: 2018 10月 72018 10月 10

出版物シリーズ

名前Proceedings - International Conference on Image Processing, ICIP
ISSN(印刷版)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
国/地域Greece
CityAthens
Period18/10/718/10/10

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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