Decomp2d: An R solution for image decomposition

Hee Seok Oh; Guebin Choi; Donghoh Kim

doi:10.1016/j.softx.2022.101182

Decomp2d: An R solution for image decomposition

Hee Seok Oh
, Guebin Choi
, Donghoh Kim^*

^*Corresponding author for this work

Department of Statistics

Research output: Contribution to journal › Journal article › peer-review

Abstract

The subjects of image processing include feature extraction, compression, denoising, image enhancement, and restoration. Many softwares have been developed for these studies. However, few studies have focused on decomposition in the literature, even though image decomposition is essential for image processing. This paper reviews several two-dimensional methods that can be adapted for image decomposition. Furthermore, we provide an R solution termed Decomp2d, tailored to the viewpoint of image decomposition based on existing R packages.

Original language	English
Article number	101182
Journal	SoftwareX
Volume	19
DOIs	https://doi.org/10.1016/j.softx.2022.101182
State	Published - 2022.07

Keywords

Decomposition
Frequency
Image
R package

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems
Data Science

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10.1016/j.softx.2022.101182

Cite this

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title = "Decomp2d: An R solution for image decomposition",

abstract = "The subjects of image processing include feature extraction, compression, denoising, image enhancement, and restoration. Many softwares have been developed for these studies. However, few studies have focused on decomposition in the literature, even though image decomposition is essential for image processing. This paper reviews several two-dimensional methods that can be adapted for image decomposition. Furthermore, we provide an R solution termed Decomp2d, tailored to the viewpoint of image decomposition based on existing R packages.",

keywords = "Decomposition, Frequency, Image, R package",

author = "Oh, \{Hee Seok\} and Guebin Choi and Donghoh Kim",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = jul,

doi = "10.1016/j.softx.2022.101182",

language = "English",

volume = "19",

journal = "SoftwareX",

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T2 - An R solution for image decomposition

AU - Oh, Hee Seok

AU - Choi, Guebin

AU - Kim, Donghoh

PY - 2022/7

Y1 - 2022/7

N2 - The subjects of image processing include feature extraction, compression, denoising, image enhancement, and restoration. Many softwares have been developed for these studies. However, few studies have focused on decomposition in the literature, even though image decomposition is essential for image processing. This paper reviews several two-dimensional methods that can be adapted for image decomposition. Furthermore, we provide an R solution termed Decomp2d, tailored to the viewpoint of image decomposition based on existing R packages.

AB - The subjects of image processing include feature extraction, compression, denoising, image enhancement, and restoration. Many softwares have been developed for these studies. However, few studies have focused on decomposition in the literature, even though image decomposition is essential for image processing. This paper reviews several two-dimensional methods that can be adapted for image decomposition. Furthermore, we provide an R solution termed Decomp2d, tailored to the viewpoint of image decomposition based on existing R packages.

KW - Decomposition

KW - Frequency

KW - Image

KW - R package

UR - https://www.scopus.com/pages/publications/85135889954

U2 - 10.1016/j.softx.2022.101182

DO - 10.1016/j.softx.2022.101182

M3 - Journal article

AN - SCOPUS:85135889954

SN - 2352-7110

VL - 19

JO - SoftwareX

JF - SoftwareX

M1 - 101182

ER -

Decomp2d: An R solution for image decomposition

Abstract

Keywords

Quacquarelli Symonds(QS) Subject Topics

Access to Document

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