Near-Infrared Image Colorization with Weighted UNet++ and Auxiliary Color Enhancement GAN

Sicong Zhou, Sei Ichiro Kamata*

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

We propose a novel GAN-based method for near-infrared image colorization. This method innovatively rebalances the color of the colorization image by importing a luminance channel and a feature weight-driven color generator. We set the weighted UNet++ structure in the generator for colorization results with the detail of focal objects. A color enhancement network composed of a deeper luminance network and a colorimetric network is used for global color balance to improve the color quality of the generated color images. Our network is trained and evaluated on two datasets. According to the FID, SSIM and PSNR results, our network performs well, with good recovery effects for both overall color and detailed color and outperforming the current state-of-the-art methods.

本文言語English
ホスト出版物のタイトル2022 7th International Conference on Image, Vision and Computing, ICIVC 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ507-512
ページ数6
ISBN(電子版)9781665467346
DOI
出版ステータスPublished - 2022
イベント7th International Conference on Image, Vision and Computing, ICIVC 2022 - Xi'an, China
継続期間: 2022 7月 262022 7月 28

出版物シリーズ

名前2022 7th International Conference on Image, Vision and Computing, ICIVC 2022

Conference

Conference7th International Conference on Image, Vision and Computing, ICIVC 2022
国/地域China
CityXi'an
Period22/7/2622/7/28

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 安全性、リスク、信頼性、品質管理

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