An Image Inpainting Method Considering Edge Connectivity of Defects

Marika Arimoto, Junichi Hara, Hiroshi Watanabe

研究成果: Conference contribution

抄録

In this paper, we propose a new image inpainting method that improves the line distortion and unnatural coloration in the restoration area observed in the conventional methods. Many deep learning inpainting methods have been proposed in recent years. However, these conventional methods have problems with distorted lines and unnatural colors in the restoration area. We solve the problems of the reconstruction of distorted lines and unnatural color simultaneously by applying edge generator from EdgeConnect to DeepFill v2. Through evaluation experiments, we show that the proposed model is better than the conventional methods in PSNR and SSIM of images with clear color boundaries and complex edges.

本文言語English
ホスト出版物のタイトル2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ101-102
ページ数2
ISBN(電子版)9781665436762
DOI
出版ステータスPublished - 2021
イベント10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
継続期間: 2021 10月 122021 10月 15

出版物シリーズ

名前2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
国/地域Japan
CityKyoto
Period21/10/1221/10/15

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 信号処理
  • 生体医工学
  • 電子工学および電気工学
  • メディア記述
  • 器械工学

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