DCT Based Texture Region Classification for Image Denoising

Seong Eui Lee; Jong Han Kim; Je Ho Ryu; Sung Min Woo; Jong Ok Kim

doi:10.1109/ICEIC54506.2022.9748659

DCT Based Texture Region Classification for Image Denoising

Seong Eui Lee, Jong Han Kim, Je Ho Ryu, Sung Min Woo, Jong Ok Kim

School of Electrical Engineering

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

Abstract

Image denoising has long been studied in literature. However, so many conventional techniques may excessively smooth texture regions during the denoising process and the original texture information is lost. This could be resolved by adjusting the strength of denoising if texture regions of a noise image were effectively classified. In this paper, we propose a new texture classification method to exploit frequency characteristics on DCT domain where texture can be easily separated from non-texture.

Original language	English
Title of host publication	2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665409346
DOIs	https://doi.org/10.1109/ICEIC54506.2022.9748659
Publication status	Published - 2022
Event	2022 International Conference on Electronics, Information, and Communication, ICEIC 2022 - Jeju, Korea, Republic of Duration: 2022 Feb 6 → 2022 Feb 9

Publication series

Name	2022 International Conference on Electronics, Information, and Communication, ICEIC 2022

Conference

Conference	2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Country/Territory	Korea, Republic of
City	Jeju
Period	22/2/6 → 22/2/9

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Information Systems and Management
Energy Engineering and Power Technology
Electrical and Electronic Engineering

Access to Document

10.1109/ICEIC54506.2022.9748659

Cite this

Lee, S. E., Kim, J. H., Ryu, J. H., Woo, S. M., & Kim, J. O. (2022). DCT Based Texture Region Classification for Image Denoising. In 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022 (2022 International Conference on Electronics, Information, and Communication, ICEIC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEIC54506.2022.9748659

DCT Based Texture Region Classification for Image Denoising. / Lee, Seong Eui; Kim, Jong Han; Ryu, Je Ho et al.

2022 International Conference on Electronics, Information, and Communication, ICEIC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 International Conference on Electronics, Information, and Communication, ICEIC 2022).

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

Lee, SE, Kim, JH, Ryu, JH, Woo, SM & Kim, JO 2022, DCT Based Texture Region Classification for Image Denoising. in 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022. 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022, Institute of Electrical and Electronics Engineers Inc., 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022, Jeju, Korea, Republic of, 22/2/6. https://doi.org/10.1109/ICEIC54506.2022.9748659

Lee SE, Kim JH, Ryu JH, Woo SM, Kim JO. DCT Based Texture Region Classification for Image Denoising. In 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 International Conference on Electronics, Information, and Communication, ICEIC 2022). doi: 10.1109/ICEIC54506.2022.9748659

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title = "DCT Based Texture Region Classification for Image Denoising",

abstract = "Image denoising has long been studied in literature. However, so many conventional techniques may excessively smooth texture regions during the denoising process and the original texture information is lost. This could be resolved by adjusting the strength of denoising if texture regions of a noise image were effectively classified. In this paper, we propose a new texture classification method to exploit frequency characteristics on DCT domain where texture can be easily separated from non-texture.",

author = "Lee, {Seong Eui} and Kim, {Jong Han} and Ryu, {Je Ho} and Woo, {Sung Min} and Kim, {Jong Ok}",

note = "Funding Information: ACKNOWLEDGMENT This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01421) supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022 ; Conference date: 06-02-2022 Through 09-02-2022",

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doi = "10.1109/ICEIC54506.2022.9748659",

language = "English",

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N1 - Funding Information: ACKNOWLEDGMENT This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01421) supervised by the IITP(Institute of Information & communications Technology Planning & Evaluation). Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Image denoising has long been studied in literature. However, so many conventional techniques may excessively smooth texture regions during the denoising process and the original texture information is lost. This could be resolved by adjusting the strength of denoising if texture regions of a noise image were effectively classified. In this paper, we propose a new texture classification method to exploit frequency characteristics on DCT domain where texture can be easily separated from non-texture.

AB - Image denoising has long been studied in literature. However, so many conventional techniques may excessively smooth texture regions during the denoising process and the original texture information is lost. This could be resolved by adjusting the strength of denoising if texture regions of a noise image were effectively classified. In this paper, we propose a new texture classification method to exploit frequency characteristics on DCT domain where texture can be easily separated from non-texture.

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DO - 10.1109/ICEIC54506.2022.9748659

M3 - Conference contribution

AN - SCOPUS:85128856972

T3 - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022

BT - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 6 February 2022 through 9 February 2022

ER -

DCT Based Texture Region Classification for Image Denoising

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