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
영상 복원을 위해 형태학적 구배로부터 얻은 사전 영상이 제안되었습니다. 수학적 형태학 분야에서는 이러한 형태학적 기울기에 사용되는 구조 요소(SE)를 유전자 알고리즘(GA)을 사용하여 최적화하는 방법도 제안되었습니다. 본 논문에서는 복원 정확도를 향상시키기 위해 이미지 복원 문제에 대한 형태학적 기울기와 전체 변형의 합인 새로운 사전 이미지를 소개합니다. 제안된 사전 이미지는 평균 제곱 오차와 같은 정량적 평가에 적합도를 거의 일치시키는 것이 가능합니다. 또한 이미지에 대한 SE의 부적합으로 인한 아티팩트 문제를 해결합니다. 제안된 영상 복원 방법의 효율성을 실험으로 보여주었다.
Shoya OOHARA
Kansai University
Mitsuji MUNEYASU
Kansai University
Soh YOSHIDA
Kansai University
Makoto NAKASHIZUKA
Chiba Institute of Technology
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Shoya OOHARA, Mitsuji MUNEYASU, Soh YOSHIDA, Makoto NAKASHIZUKA, "Image Regularization with Total Variation and Optimized Morphological Gradient Priors" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 12, pp. 1920-1924, December 2019, doi: 10.1587/transfun.E102.A.1920.
Abstract: For image restoration, an image prior that is obtained from the morphological gradient has been proposed. In the field of mathematical morphology, the optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. In this paper, we introduce a new image prior that is the sum of the morphological gradients and total variation for an image restoration problem to improve the restoration accuracy. The proposed image prior makes it possible to almost match the fitness to a quantitative evaluation such as the mean square error. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1920/_p
부
@ARTICLE{e102-a_12_1920,
author={Shoya OOHARA, Mitsuji MUNEYASU, Soh YOSHIDA, Makoto NAKASHIZUKA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Image Regularization with Total Variation and Optimized Morphological Gradient Priors},
year={2019},
volume={E102-A},
number={12},
pages={1920-1924},
abstract={For image restoration, an image prior that is obtained from the morphological gradient has been proposed. In the field of mathematical morphology, the optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. In this paper, we introduce a new image prior that is the sum of the morphological gradients and total variation for an image restoration problem to improve the restoration accuracy. The proposed image prior makes it possible to almost match the fitness to a quantitative evaluation such as the mean square error. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.},
keywords={},
doi={10.1587/transfun.E102.A.1920},
ISSN={1745-1337},
month={December},}
부
TY - JOUR
TI - Image Regularization with Total Variation and Optimized Morphological Gradient Priors
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1920
EP - 1924
AU - Shoya OOHARA
AU - Mitsuji MUNEYASU
AU - Soh YOSHIDA
AU - Makoto NAKASHIZUKA
PY - 2019
DO - 10.1587/transfun.E102.A.1920
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2019
AB - For image restoration, an image prior that is obtained from the morphological gradient has been proposed. In the field of mathematical morphology, the optimization of the structuring element (SE) used for this morphological gradient using a genetic algorithm (GA) has also been proposed. In this paper, we introduce a new image prior that is the sum of the morphological gradients and total variation for an image restoration problem to improve the restoration accuracy. The proposed image prior makes it possible to almost match the fitness to a quantitative evaluation such as the mean square error. It also solves the problem of the artifact due to the unsuitability of the SE for the image. An experiment shows the effectiveness of the proposed image restoration method.
ER -