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

Takuro Matsui, Masaaki Ikehara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9789082797053
Publication statusPublished - 2021 Jan 24
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 2020 Aug 242020 Aug 28

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference28th European Signal Processing Conference, EUSIPCO 2020


  • Deep learning
  • Generative adversarial network
  • Image restoration
  • Residual learning
  • Single-image de-raining

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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