Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-6 |
| Number of pages | 6 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781538619377 |
| DOIs | |
| Publication status | Published - 2018 Feb 22 |
| Event | 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 - Shanghai, China Duration: 2017 Oct 14 → 2017 Oct 16 |
Other
| Other | 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 17/10/14 → 17/10/16 |
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Keywords
- Auto Cdoer-Decoder (CODEC) Model
- Data-Dependent Feature Extractor (DDFE)
- Sparse Representation
- Super Resolution (SR)
ASJC Scopus subject areas
- Health Informatics
- Artificial Intelligence
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
Cite this
Auto coder-decoder (CODEC) model based sparse representation for image super resolution. / Zhang, Qieshi; Gu, Liyan; Cheng, Jun; Wu, Xiaojun; Kamata, Seiichiro.
Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Auto coder-decoder (CODEC) model based sparse representation for image super resolution
AU - Zhang, Qieshi
AU - Gu, Liyan
AU - Cheng, Jun
AU - Wu, Xiaojun
AU - Kamata, Seiichiro
PY - 2018/2/22
Y1 - 2018/2/22
N2 - 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.
AB - 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.
KW - Auto Cdoer-Decoder (CODEC) Model
KW - Data-Dependent Feature Extractor (DDFE)
KW - Sparse Representation
KW - Super Resolution (SR)
UR - http://www.scopus.com/inward/record.url?scp=85047635693&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047635693&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI.2017.8301950
DO - 10.1109/CISP-BMEI.2017.8301950
M3 - Conference contribution
AN - SCOPUS:85047635693
VL - 2018-January
SP - 1
EP - 6
BT - Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -