Infrared Image Colorization Using a S-Shape Network

Ziyue Dong, Sei Ichiro Kamata, Toby P. Breckon

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

This paper proposes a novel approach for colorizing near infrared (NIR) images using a S-shape network (SNet). The proposed approach is based on the usage of an encoder-decoder architecture followed with a secondary assistant network. The encoder-decoder consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. The assistant network is a shallow encoder-decoder to enhance the edge and improve the output, which can be trained end-to-end from a few image examples. The trained model does not require any user guidance or a reference image database. Furthermore, our architecture will preserve clear edges within NIR images. Our overall architecture is trained and evaluated on a real-world dataset containing a significant amount of road scene images. This dataset was captured by a NIR camera and a corresponding RGB camera to facilitate side-by-side comparison. In the experiments, we demonstrate that our SNet works well, and outperforms contemporary state-of-the-art approaches.

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2242-2246
ページ数5
ISBN(電子版)9781479970612
DOI
出版ステータスPublished - 2018 8 29
イベント25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
継続期間: 2018 10 72018 10 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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