IEICE TRANSACTIONS on Fundamentals
Noise Removal Based on Surface Approximation of Color Line
Summary :
In a local region of a color image, the color distribution often takes the form of a linear line in the RGB space. This property is called “Color Line” and we propose a denoising method based on this property. When a noise is added on an image, its color distribution spreads from the Color Line. The denoising is achieved by reducing the spread. In conventional methods, Color Line is assumed to be only a single line, but actual distribution takes various shapes such as a single line, two lines, and a plane and so on. In our method, we estimate the distribution in more detail using plane approximation and denoise each patch by reducing the spread depending on the Color Line types. In this way, we can achieve better denoising results than a conventional method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.9 pp.1567-1574
- Publication Date
- 2018/09/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.1567
- Type of Manuscript
- PAPER
- Category
- Image
Authors
Koichiro MANABE
Keio University
Takuro YAMAGUCHI
Keio University
Masaaki IKEHARA
Keio University
Keyword
Latest Issue
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Koichiro MANABE, Takuro YAMAGUCHI, Masaaki IKEHARA, "Noise Removal Based on Surface Approximation of Color Line" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 9, pp. 1567-1574, September 2018, doi: 10.1587/transfun.E101.A.1567.
Abstract: In a local region of a color image, the color distribution often takes the form of a linear line in the RGB space. This property is called “Color Line” and we propose a denoising method based on this property. When a noise is added on an image, its color distribution spreads from the Color Line. The denoising is achieved by reducing the spread. In conventional methods, Color Line is assumed to be only a single line, but actual distribution takes various shapes such as a single line, two lines, and a plane and so on. In our method, we estimate the distribution in more detail using plane approximation and denoise each patch by reducing the spread depending on the Color Line types. In this way, we can achieve better denoising results than a conventional method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1567/_p
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@ARTICLE{e101-a_9_1567,
author={Koichiro MANABE, Takuro YAMAGUCHI, Masaaki IKEHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Noise Removal Based on Surface Approximation of Color Line},
year={2018},
volume={E101-A},
number={9},
pages={1567-1574},
abstract={In a local region of a color image, the color distribution often takes the form of a linear line in the RGB space. This property is called “Color Line” and we propose a denoising method based on this property. When a noise is added on an image, its color distribution spreads from the Color Line. The denoising is achieved by reducing the spread. In conventional methods, Color Line is assumed to be only a single line, but actual distribution takes various shapes such as a single line, two lines, and a plane and so on. In our method, we estimate the distribution in more detail using plane approximation and denoise each patch by reducing the spread depending on the Color Line types. In this way, we can achieve better denoising results than a conventional method.},
keywords={},
doi={10.1587/transfun.E101.A.1567},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Noise Removal Based on Surface Approximation of Color Line
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1567
EP - 1574
AU - Koichiro MANABE
AU - Takuro YAMAGUCHI
AU - Masaaki IKEHARA
PY - 2018
DO - 10.1587/transfun.E101.A.1567
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2018
AB - In a local region of a color image, the color distribution often takes the form of a linear line in the RGB space. This property is called “Color Line” and we propose a denoising method based on this property. When a noise is added on an image, its color distribution spreads from the Color Line. The denoising is achieved by reducing the spread. In conventional methods, Color Line is assumed to be only a single line, but actual distribution takes various shapes such as a single line, two lines, and a plane and so on. In our method, we estimate the distribution in more detail using plane approximation and denoise each patch by reducing the spread depending on the Color Line types. In this way, we can achieve better denoising results than a conventional method.
ER -
English
