GAN-Based Rain Noise Removal from Single-Image Considering Rain Composite Models

Takuro Matsui, Masaaki Ikehara

研究成果: Article査読

5 被引用数 (Scopus)

抄録

Under severe weather conditions, outdoor images or videos captured by cameras can be affected by heavy rain and fog. For example, on a rainy day, autonomous vehicles have difficulty determining how to navigate due to the degraded visual quality of images. 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 experimental results show that our method is suitable for a wide range of rainy images. 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
論文番号9016251
ページ(範囲)40892-40900
ページ数9
ジャーナルIEEE Access
8
DOI
出版ステータスPublished - 2020

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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