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

Qieshi Zhang, Liyan Gu, Jun Cheng, Xiaojun Wu, Seiichiro Kamata

研究成果: Conference contribution

抜粋

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.

元の言語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
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
2018-January
ISBN(電子版)9781538619377
DOI
出版物ステータス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

Other

Other10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
China
Shanghai
期間17/10/1417/10/16

ASJC Scopus subject areas

  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering

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  • これを引用

    Zhang, Q., Gu, L., Cheng, J., Wu, X., & Kamata, S. (2018). Auto coder-decoder (CODEC) model based sparse representation for image super resolution. : Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 (巻 2018-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISP-BMEI.2017.8301950