Single Image Super-resolution with Self-similarity

Yoojun Nam; Junwon Mun; Yunseok Jang; Jaeseok Kim

doi:10.1109/ICCE.2019.8662051

Single Image Super-resolution with Self-similarity

Yoojun Nam, Junwon Mun, Yunseok Jang, Jaeseok Kim

Department of Electrical and Electronic Engineering

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

3 Citations (Scopus)

Abstract

Degraded low-resolution (LR) images are often obtained from cameras. Resolution enhancement and image restoration are very practical in many fields such as medical imaging, surveillance system and remote sensing. Single image super-resolution is a technique which reconstruct a restored high-resolution (HR) image from a degraded LR image. In this paper, we propose single image super-resolution based on sparse coding using self-similarity prior. A sparsity constraint is used to jointly train coupled dictionaries which can generate high frequency details. Reconstructed HR output is enhanced with non-local means based on self-similarity prior. Experimental results demonstrate that our method shows higher performance than other existing algorithms.

Original language	English
Title of host publication	2019 IEEE International Conference on Consumer Electronics, ICCE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538679104
DOIs	https://doi.org/10.1109/ICCE.2019.8662051
Publication status	Published - 2019 Mar 6
Event	2019 IEEE International Conference on Consumer Electronics, ICCE 2019 - Las Vegas, United States Duration: 2019 Jan 11 → 2019 Jan 13

Publication series

Name	2019 IEEE International Conference on Consumer Electronics, ICCE 2019

Conference

Conference	2019 IEEE International Conference on Consumer Electronics, ICCE 2019
Country/Territory	United States
City	Las Vegas
Period	19/1/11 → 19/1/13

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering
Media Technology
Electrical and Electronic Engineering

Access to Document

10.1109/ICCE.2019.8662051

Cite this

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title = "Single Image Super-resolution with Self-similarity",

abstract = "Degraded low-resolution (LR) images are often obtained from cameras. Resolution enhancement and image restoration are very practical in many fields such as medical imaging, surveillance system and remote sensing. Single image super-resolution is a technique which reconstruct a restored high-resolution (HR) image from a degraded LR image. In this paper, we propose single image super-resolution based on sparse coding using self-similarity prior. A sparsity constraint is used to jointly train coupled dictionaries which can generate high frequency details. Reconstructed HR output is enhanced with non-local means based on self-similarity prior. Experimental results demonstrate that our method shows higher performance than other existing algorithms.",

author = "Yoojun Nam and Junwon Mun and Yunseok Jang and Jaeseok Kim",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Consumer Electronics, ICCE 2019 ; Conference date: 11-01-2019 Through 13-01-2019",

year = "2019",

month = mar,

day = "6",

doi = "10.1109/ICCE.2019.8662051",

language = "English",

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Nam, Y, Mun, J, Jang, Y & Kim, J 2019, Single Image Super-resolution with Self-similarity. in 2019 IEEE International Conference on Consumer Electronics, ICCE 2019., 8662051, 2019 IEEE International Conference on Consumer Electronics, ICCE 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Consumer Electronics, ICCE 2019, Las Vegas, United States, 19/1/11. https://doi.org/10.1109/ICCE.2019.8662051

Single Image Super-resolution with Self-similarity. / Nam, Yoojun; Mun, Junwon; Jang, Yunseok et al.
2019 IEEE International Conference on Consumer Electronics, ICCE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8662051 (2019 IEEE International Conference on Consumer Electronics, ICCE 2019).

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

TY - GEN

T1 - Single Image Super-resolution with Self-similarity

AU - Nam, Yoojun

AU - Mun, Junwon

AU - Jang, Yunseok

AU - Kim, Jaeseok

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N2 - Degraded low-resolution (LR) images are often obtained from cameras. Resolution enhancement and image restoration are very practical in many fields such as medical imaging, surveillance system and remote sensing. Single image super-resolution is a technique which reconstruct a restored high-resolution (HR) image from a degraded LR image. In this paper, we propose single image super-resolution based on sparse coding using self-similarity prior. A sparsity constraint is used to jointly train coupled dictionaries which can generate high frequency details. Reconstructed HR output is enhanced with non-local means based on self-similarity prior. Experimental results demonstrate that our method shows higher performance than other existing algorithms.

AB - Degraded low-resolution (LR) images are often obtained from cameras. Resolution enhancement and image restoration are very practical in many fields such as medical imaging, surveillance system and remote sensing. Single image super-resolution is a technique which reconstruct a restored high-resolution (HR) image from a degraded LR image. In this paper, we propose single image super-resolution based on sparse coding using self-similarity prior. A sparsity constraint is used to jointly train coupled dictionaries which can generate high frequency details. Reconstructed HR output is enhanced with non-local means based on self-similarity prior. Experimental results demonstrate that our method shows higher performance than other existing algorithms.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Consumer Electronics, ICCE 2019

Y2 - 11 January 2019 through 13 January 2019

ER -

Single Image Super-resolution with Self-similarity

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

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