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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

Shoya OOHARA

Kansai University

Mitsuji MUNEYASU

Kansai University

Soh YOSHIDA

Kansai University

Makoto NAKASHIZUKA

Chiba Institute of Technology

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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

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@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},}

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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 -