IEICE TRANSACTIONS on Fundamentals
Image Regularization with Total Variation and Optimized Morphological Gradient Priors
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.12 pp.1920-1924
- Publication Date
- 2019/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E102.A.1920
- Type of Manuscript
- Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)
- Category
- Image
Authors
Shoya OOHARA
Kansai University
Mitsuji MUNEYASU
Kansai University
Soh YOSHIDA
Kansai University
Makoto NAKASHIZUKA
Chiba Institute of Technology
Keyword
Latest Issue
Copyrights notice of machine-translated contents
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.
Cite this
Copy
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
Copy
@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},}
Copy
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 -
English
