Image Quality Improvement for Capsule Endoscopy Based on Compressed Sensing with K-SVD Dictionary Learning

Harada, Yuuki; Kanemoto, Daisuke; Inoue, Takahiro et al.

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2022, E105A(4), 743-747

アクセス数:46件（2025-04-18 20:37 集計）

固定URL: https://hdl.handle.net/11094/97804

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論文情報

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タイトル
Image Quality Improvement for Capsule Endoscopy Based on Compressed Sensing with K-SVD Dictionary Learning
著者
著者名
Harada, Yuuki
NRID
1000090603332 1000090603332
researchmap permalink
dkanemoto dkanemoto
Scopus Author ID
6504308983 6504308983
著者名
Kanemoto, Daisuke
NRID
1000030648184 1000030648184
researchmap permalink
in0ue in0ue
Scopus Author ID
55246263200 55246263200
著者名
Inoue, Takahiro
NRID
1000040346177 1000040346177
researchmap permalink
osamu_maida osamu_maida
Scopus Author ID
6602733779 6602733779
著者名
Maida, Osamu
NRID
1000070396315 1000070396315
researchmap permalink
hirose_tetsuya hirose_tetsuya
Scopus Author ID
9272478400 9272478400
著者名
Hirose, Tetsuya
キーワード等
capsule endoscopy
compressed sensing
high quality
K-SVD
内容
Harada Y., Kanemoto D., Inoue T., et al. Image Quality Improvement for Capsule Endoscopy Based on Compressed Sensing with K-SVD Dictionary Learning. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E105.A, 743 (2022); https://doi.org/10.1587/transfun.2021EAL2033.
抄録
Reducing the power consumption of capsule endoscopy is essential for its further development. We introduce K-SVD dictionary learning to design a dictionary for sparse coding, and improve reconstruction accuracy of capsule endoscopic images captured using compressed sensing. At a compression ratio of 20%, the proposed method improves image quality by approximately 4.4 dB for the peak signal-to-noise ratio.
出版者
電子情報通信学会
Institute of Electronics Information Communication Engineers
収録物名
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
巻(号)
E105A(4)
ページ
743-747
刊行年月
2022-04-01
言語
英語
ハンドルURL
https://hdl.handle.net/11094/97804
PISSN
09168508
eISSN
17451337
関連情報 (isIdenticalTo)
DOI (出版社版)
https://doi.org/10.1587/transfun.2021EAL2033
アクセス権
open access
権利情報
Copyright © 2022 The Institute of Electronics, Information and Communication Engineers
出版タイプ
Version of Record
カテゴリ
学術雑誌論文 Journal Article