An Adaptation Method for Morphological Opening Filters with a Smoothness Penalty on Structuring Elements

Makoto NAKASHIZUKA; Yu ASHIHARA; Youji IIGUNI

doi:10.1587/transfun.E96.A.1468

An Adaptation Method for Morphological Opening Filters with a Smoothness Penalty on Structuring Elements

Makoto NAKASHIZUKA, Yu ASHIHARA, Youji IIGUNI

Full Text Views

0

Share
Cite this

Summary :

This paper proposes an adaptation method for structuring elements of morphological filters. A structuring element of a morphological filter specifies a shape of local structures that is eliminated or preserved in the output. The adaptation of the structuring element is hence a crucial problem for image denoising using morphological filters. Existing adaptation methods for structuring elements require preliminary training using example images. We propose an adaptation method for structuring elements of morphological opening filters that does not require such training. In our approach, the opening filter is interpreted as an approximation method with the union of the structuring elements. In order to eliminate noise components, a penalty defined from an assumption of image smoothness is imposed on the structuring element. Image denoising is achieved through decreasing the objective function, which is the sum of an approximation error term and the penalty function. In experiments, we use the proposed method to demonstrate positive impulsive noise reduction from images.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E96-A No.6 pp.1468-1477

Publication Date: 2013/06/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E96.A.1468

Type of Manuscript: PAPER

Category: Image

Authors

Makoto NAKASHIZUKA
  Chiba Institute of Technology
Yu ASHIHARA
  Osaka University
Youji IIGUNI
  Osaka University

Keyword

image recovery, mathematical morphology, regularization, denoising

Cite this

Copy

Makoto NAKASHIZUKA, Yu ASHIHARA, Youji IIGUNI, "An Adaptation Method for Morphological Opening Filters with a Smoothness Penalty on Structuring Elements" in IEICE TRANSACTIONS on Fundamentals, vol. E96-A, no. 6, pp. 1468-1477, June 2013, doi: 10.1587/transfun.E96.A.1468.
Abstract: This paper proposes an adaptation method for structuring elements of morphological filters. A structuring element of a morphological filter specifies a shape of local structures that is eliminated or preserved in the output. The adaptation of the structuring element is hence a crucial problem for image denoising using morphological filters. Existing adaptation methods for structuring elements require preliminary training using example images. We propose an adaptation method for structuring elements of morphological opening filters that does not require such training. In our approach, the opening filter is interpreted as an approximation method with the union of the structuring elements. In order to eliminate noise components, a penalty defined from an assumption of image smoothness is imposed on the structuring element. Image denoising is achieved through decreasing the objective function, which is the sum of an approximation error term and the penalty function. In experiments, we use the proposed method to demonstrate positive impulsive noise reduction from images.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E96.A.1468/_p

Copy

@ARTICLE{e96-a_6_1468,
author={Makoto NAKASHIZUKA, Yu ASHIHARA, Youji IIGUNI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Adaptation Method for Morphological Opening Filters with a Smoothness Penalty on Structuring Elements},
year={2013},
volume={E96-A},
number={6},
pages={1468-1477},
abstract={This paper proposes an adaptation method for structuring elements of morphological filters. A structuring element of a morphological filter specifies a shape of local structures that is eliminated or preserved in the output. The adaptation of the structuring element is hence a crucial problem for image denoising using morphological filters. Existing adaptation methods for structuring elements require preliminary training using example images. We propose an adaptation method for structuring elements of morphological opening filters that does not require such training. In our approach, the opening filter is interpreted as an approximation method with the union of the structuring elements. In order to eliminate noise components, a penalty defined from an assumption of image smoothness is imposed on the structuring element. Image denoising is achieved through decreasing the objective function, which is the sum of an approximation error term and the penalty function. In experiments, we use the proposed method to demonstrate positive impulsive noise reduction from images.},
keywords={},
doi={10.1587/transfun.E96.A.1468},
ISSN={1745-1337},
month={June},}

Copy

TY - JOUR
TI - An Adaptation Method for Morphological Opening Filters with a Smoothness Penalty on Structuring Elements
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1468
EP - 1477
AU - Makoto NAKASHIZUKA
AU - Yu ASHIHARA
AU - Youji IIGUNI
PY - 2013
DO - 10.1587/transfun.E96.A.1468
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E96-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2013
AB - This paper proposes an adaptation method for structuring elements of morphological filters. A structuring element of a morphological filter specifies a shape of local structures that is eliminated or preserved in the output. The adaptation of the structuring element is hence a crucial problem for image denoising using morphological filters. Existing adaptation methods for structuring elements require preliminary training using example images. We propose an adaptation method for structuring elements of morphological opening filters that does not require such training. In our approach, the opening filter is interpreted as an approximation method with the union of the structuring elements. In order to eliminate noise components, a penalty defined from an assumption of image smoothness is imposed on the structuring element. Image denoising is achieved through decreasing the objective function, which is the sum of an approximation error term and the penalty function. In experiments, we use the proposed method to demonstrate positive impulsive noise reduction from images.
ER -