The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
캡슐내시경의 발전을 위해서는 전력소모를 줄이는 것이 필수적이다. 희소 코딩을 위한 사전을 설계하고 압축 감지를 사용하여 캡처한 캡슐 내시경 영상의 재구성 정확도를 향상시키기 위해 K-SVD 사전 학습을 도입합니다. 제안된 방법은 압축률 20%에서 최대 신호 대 잡음비에 대해 약 4.4dB 정도 영상 품질을 향상시킨다.
Yuuki HARADA
Osaka University
Daisuke KANEMOTO
Osaka University
Takahiro INOUE
Osaka University
Osamu MAIDA
Osaka University
Tetsuya HIROSE
Osaka University
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Yuuki HARADA, Daisuke KANEMOTO, Takahiro INOUE, Osamu MAIDA, Tetsuya HIROSE, "Image Quality Improvement for Capsule Endoscopy Based on Compressed Sensing with K-SVD Dictionary Learning" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 4, pp. 743-747, April 2022, doi: 10.1587/transfun.2021EAL2033.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAL2033/_p
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@ARTICLE{e105-a_4_743,
author={Yuuki HARADA, Daisuke KANEMOTO, Takahiro INOUE, Osamu MAIDA, Tetsuya HIROSE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Image Quality Improvement for Capsule Endoscopy Based on Compressed Sensing with K-SVD Dictionary Learning},
year={2022},
volume={E105-A},
number={4},
pages={743-747},
abstract={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.},
keywords={},
doi={10.1587/transfun.2021EAL2033},
ISSN={1745-1337},
month={April},}
부
TY - JOUR
TI - Image Quality Improvement for Capsule Endoscopy Based on Compressed Sensing with K-SVD Dictionary Learning
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 743
EP - 747
AU - Yuuki HARADA
AU - Daisuke KANEMOTO
AU - Takahiro INOUE
AU - Osamu MAIDA
AU - Tetsuya HIROSE
PY - 2022
DO - 10.1587/transfun.2021EAL2033
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2022
AB - 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.
ER -