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

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

doi:10.1109/CISP-BMEI.2017.8301950

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

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

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1109/CISP-BMEI.2017.8301950
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

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

Access to Document

10.1109/CISP-BMEI.2017.8301950

Fingerprint Dive into the research topics of 'Auto coder-decoder (CODEC) model based sparse representation for image super resolution'. Together they form a unique fingerprint.

Cite this

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

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

Zhang, Q, Gu, L, Cheng, J, Wu, X & Kamata, S 2018, Auto coder-decoder (CODEC) model based sparse representation for image super resolution. in 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., pp. 1-6, 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017, Shanghai, China, 17/10/14. https://doi.org/10.1109/CISP-BMEI.2017.8301950

Zhang Q, Gu L, Cheng J, Wu X, Kamata S. Auto coder-decoder (CODEC) model based sparse representation for image super resolution. In 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 https://doi.org/10.1109/CISP-BMEI.2017.8301950

Zhang, Qieshi ; Gu, Liyan ; Cheng, Jun ; Wu, Xiaojun ; Kamata, Seiichiro. / 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. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6

@inproceedings{639b7cf4eb9b4efaa467262a4b43f805,

title = "Auto coder-decoder (CODEC) model based sparse representation for image super resolution",

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.",

keywords = "Auto Cdoer-Decoder (CODEC) Model, Data-Dependent Feature Extractor (DDFE), Sparse Representation, Super Resolution (SR)",

author = "Qieshi Zhang and Liyan Gu and Jun Cheng and Xiaojun Wu and Seiichiro Kamata",

year = "2018",

month = feb,

day = "22",

doi = "10.1109/CISP-BMEI.2017.8301950",

language = "English",

volume = "2018-January",

pages = "1--6",

booktitle = "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.",

note = "10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 ; Conference date: 14-10-2017 Through 16-10-2017",

}

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.

T2 - 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017

Y2 - 14 October 2017 through 16 October 2017

ER -