Scale Recurrent Network for Single Image Dehazing

Wataru Imai, Yosuke Ueki, Masaaki Ikehara

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

The quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the image, haze removal methods play a critical role. In this paper, we propose a new multi-scale network structure that is rarely used in haze removal and a new loss function to generate high-quality haze-free images. Despite a number of proposed dehazing methods, they are not able to capture the haze accurately without being confused by other objects that are similar in color to haze and completely remove the haze throughout the image. Our proposed network architecture takes the Scale Recurrent Network structure and we incorporate dilated convolution to catch the more global features like haze with the wide receptive field. Additionally, we train our network with a new loss function using dark channel prior to more effectively learn the haze features. 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2022 IEEE International Conference on Consumer Electronics, ICCE 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665441544
DOI
出版ステータスPublished - 2022
イベント2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States
継続期間: 2022 1月 72022 1月 9

出版物シリーズ

名前Digest of Technical Papers - IEEE International Conference on Consumer Electronics
2022-January
ISSN(印刷版)0747-668X

Conference

Conference2022 IEEE International Conference on Consumer Electronics, ICCE 2022
国/地域United States
CityVirtual, Online
Period22/1/722/1/9

ASJC Scopus subject areas

  • 産業および生産工学
  • 電子工学および電気工学

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