Auto coder-decoder (CODEC) model based sparse representation for image super resolution

Qieshi Zhang, Liyan Gu, Jun Cheng, Xiaojun Wu*, Sei Ichiro Kamata

*この研究の対応する著者

研究成果

抄録

In our daily life, the high quality image is widely used in varieties of fields, but sometimes we cannot capture the image with idea resolution due to some influences. For solving the resolution limitation of imaging sensors, the image super resolution (SR) representation technology is widely researched. Considering the advantage of sparse representation, the dictionary learning based methods is widely studied. However, landmark atoms cannot provide the representations of images, since the general feature extractors is universally applicable in feature extraction. To overcome the drawbacks, an auto coder-decoder (CODEC) model is proposed to extract representative features from low resolution (LR) images. The experimental results indicate the proposed method can obtain better effect than other methods.

本文言語English
ホスト出版物のタイトルProceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
編集者Song Qiu, Hongying Liu, Li Sun, Lipo Wang, Qingli Li, Mei Zhou
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
ISBN(電子版)9781538619377
DOI
出版ステータスPublished - 2018 2 22
イベント10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 - Shanghai, China
継続期間: 2017 10 142017 10 16

出版物シリーズ

名前Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
2018-January

Other

Other10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
国/地域China
CityShanghai
Period17/10/1417/10/16

ASJC Scopus subject areas

  • 健康情報学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理
  • 生体医工学

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