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
Di kawasan tempatan imej warna, taburan warna selalunya berbentuk garisan linear dalam ruang RGB. Hartanah ini dipanggil "Garisan Warna" dan kami mencadangkan kaedah denoising berdasarkan sifat ini. Apabila bunyi ditambah pada imej, taburan warnanya merebak daripada Garisan Warna. Denoising dicapai dengan mengurangkan penyebaran. Dalam kaedah konvensional, Garis Warna diandaikan hanya satu garisan, tetapi taburan sebenar mengambil pelbagai bentuk seperti satu garisan, dua garisan, dan satah dan sebagainya. Dalam kaedah kami, kami menganggarkan taburan dengan lebih terperinci menggunakan anggaran satah dan mengecilkan setiap tampalan dengan mengurangkan sebaran bergantung pada jenis Garis Warna. Dengan cara ini, kita boleh mencapai hasil denoising yang lebih baik daripada kaedah konvensional.
Koichiro MANABE
Keio University
Takuro YAMAGUCHI
Keio University
Masaaki IKEHARA
Keio University
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Salinan
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
Salinan
@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},}
Salinan
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 -