A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising

Seonghyeon Nam; Youngbae Hwang; Yasuyuki Matsushita; Seon Joo Kim

doi:10.1109/CVPR.2016.186

A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising

Seonghyeon Nam, Youngbae Hwang, Yasuyuki Matsushita, Seon Joo Kim

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

82 Citations (Scopus)

Abstract

Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different color channels get mixed during the imaging process inside the camera due to gamut mapping, tone-mapping, and compression. We show the influence of the in-camera imaging pipeline on noise and propose a new noise model in the 3D RGB space to accounts for the color channel mix-ups. A data-driven approach for determining the parameters of the new noise model is introduced as well as its application to image denoising. The experiments show that our noise model represents the noise in regular JPEG images more accurately compared to the previous models and is advantageous in image denoising.

Original language	English
Title of host publication	Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Publisher	IEEE Computer Society
Pages	1683-1691
Number of pages	9
ISBN (Electronic)	9781467388504
DOIs	https://doi.org/10.1109/CVPR.2016.186
Publication status	Published - 2016 Dec 9
Event	29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States Duration: 2016 Jun 26 → 2016 Jul 1

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2016-December
ISSN (Print)	1063-6919

Conference

Conference	29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Country	United States
City	Las Vegas
Period	16/6/26 → 16/7/1

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2016.186

Fingerprint Dive into the research topics of 'A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising'. Together they form a unique fingerprint.

Cite this

Nam, S., Hwang, Y., Matsushita, Y., & Kim, S. J. (2016). A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 1683-1691). [7780555] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.186

Nam, Seonghyeon ; Hwang, Youngbae ; Matsushita, Yasuyuki ; Kim, Seon Joo. / A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising. Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. IEEE Computer Society, 2016. pp. 1683-1691 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

@inproceedings{feae58e02d6c4414a7c3ba66572ea0fd,

title = "A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising",

abstract = "Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different color channels get mixed during the imaging process inside the camera due to gamut mapping, tone-mapping, and compression. We show the influence of the in-camera imaging pipeline on noise and propose a new noise model in the 3D RGB space to accounts for the color channel mix-ups. A data-driven approach for determining the parameters of the new noise model is introduced as well as its application to image denoising. The experiments show that our noise model represents the noise in regular JPEG images more accurately compared to the previous models and is advantageous in image denoising.",

author = "Seonghyeon Nam and Youngbae Hwang and Yasuyuki Matsushita and Kim, {Seon Joo}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 ; Conference date: 26-06-2016 Through 01-07-2016",

year = "2016",

month = dec,

day = "9",

doi = "10.1109/CVPR.2016.186",

language = "English",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

publisher = "IEEE Computer Society",

pages = "1683--1691",

booktitle = "Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016",

address = "United States",

}

Nam, S, Hwang, Y, Matsushita, Y & Kim, SJ 2016, A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising. in Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016., 7780555, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, IEEE Computer Society, pp. 1683-1691, 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, United States, 16/6/26. https://doi.org/10.1109/CVPR.2016.186

A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising. / Nam, Seonghyeon; Hwang, Youngbae; Matsushita, Yasuyuki; Kim, Seon Joo.

Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. IEEE Computer Society, 2016. p. 1683-1691 7780555 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising

AU - Nam, Seonghyeon

AU - Hwang, Youngbae

AU - Matsushita, Yasuyuki

AU - Kim, Seon Joo

PY - 2016/12/9

Y1 - 2016/12/9

N2 - Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different color channels get mixed during the imaging process inside the camera due to gamut mapping, tone-mapping, and compression. We show the influence of the in-camera imaging pipeline on noise and propose a new noise model in the 3D RGB space to accounts for the color channel mix-ups. A data-driven approach for determining the parameters of the new noise model is introduced as well as its application to image denoising. The experiments show that our noise model represents the noise in regular JPEG images more accurately compared to the previous models and is advantageous in image denoising.

AB - Modelling and analyzing noise in images is a fundamental task in many computer vision systems. Traditionally, noise has been modelled per color channel assuming that the color channels are independent. Although the color channels can be considered as mutually independent in camera RAW images, signals from different color channels get mixed during the imaging process inside the camera due to gamut mapping, tone-mapping, and compression. We show the influence of the in-camera imaging pipeline on noise and propose a new noise model in the 3D RGB space to accounts for the color channel mix-ups. A data-driven approach for determining the parameters of the new noise model is introduced as well as its application to image denoising. The experiments show that our noise model represents the noise in regular JPEG images more accurately compared to the previous models and is advantageous in image denoising.

UR - http://www.scopus.com/inward/record.url?scp=84986277826&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84986277826&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2016.186

DO - 10.1109/CVPR.2016.186

M3 - Conference contribution

AN - SCOPUS:84986277826

T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

SP - 1683

EP - 1691

BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

PB - IEEE Computer Society

T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

Y2 - 26 June 2016 through 1 July 2016

ER -

Nam S, Hwang Y, Matsushita Y, Kim SJ. A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. IEEE Computer Society. 2016. p. 1683-1691. 7780555. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2016.186

A Holistic Approach to Cross-Channel Image Noise Modeling and Its Application to Image Denoising

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Cite this