Learning Camera-Aware Noise Models

Ke Chi Chang; Ren Wang; Hung Jin Lin; Yu Lun Liu; Chia Ping Chen; Yu Lin Chang; Hwann Tzong Chen

doi:10.1007/978-3-030-58586-0_21

Learning Camera-Aware Noise Models

Ke Chi Chang, Ren Wang, Hung Jin Lin, Yu Lun Liu, Chia Ping Chen, Yu Lin Chang, Hwann Tzong Chen^*

^*Corresponding author for this work

Institute of Computer Science and Engineering

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

21 Scopus citations

Abstract

Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/.

Original language	English
Title of host publication	Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
Editors	Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	343-358
Number of pages	16
ISBN (Print)	9783030585853
DOIs	https://doi.org/10.1007/978-3-030-58586-0_21
State	Published - 2020
Event	16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12369 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th European Conference on Computer Vision, ECCV 2020
Country/Territory	United Kingdom
City	Glasgow
Period	23/08/20 → 28/08/20

Keywords

Denoising
GANs
Noise model
Sensor

Access to Document

10.1007/978-3-030-58586-0_21

Cite this

Chang, K. C., Wang, R., Lin, H. J., Liu, Y. L., Chen, C. P., Chang, Y. L., & Chen, H. T. (2020). Learning Camera-Aware Noise Models. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings (pp. 343-358). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12369 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58586-0_21

Chang, Ke Chi ; Wang, Ren ; Lin, Hung Jin et al. / Learning Camera-Aware Noise Models. Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. editor / Andrea Vedaldi ; Horst Bischof ; Thomas Brox ; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. pp. 343-358 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{7e9899d06f06442eac82f1be1b6fa668,

title = "Learning Camera-Aware Noise Models",

abstract = "Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/.",

keywords = "Denoising, GANs, Noise model, Sensor",

author = "Chang, {Ke Chi} and Ren Wang and Lin, {Hung Jin} and Liu, {Yu Lun} and Chen, {Chia Ping} and Chang, {Yu Lin} and Chen, {Hwann Tzong}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-58586-0_21",

language = "English",

isbn = "9783030585853",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "343--358",

editor = "Andrea Vedaldi and Horst Bischof and Thomas Brox and Jan-Michael Frahm",

booktitle = "Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings",

address = "德國",

}

Chang, KC, Wang, R, Lin, HJ, Liu, YL, Chen, CP, Chang, YL & Chen, HT 2020, Learning Camera-Aware Noise Models. in A Vedaldi, H Bischof, T Brox & J-M Frahm (eds), Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12369 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 343-358, 16th European Conference on Computer Vision, ECCV 2020, Glasgow, United Kingdom, 23/08/20. https://doi.org/10.1007/978-3-030-58586-0_21

Learning Camera-Aware Noise Models. / Chang, Ke Chi; Wang, Ren; Lin, Hung Jin et al.
Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. ed. / Andrea Vedaldi; Horst Bischof; Thomas Brox; Jan-Michael Frahm. Springer Science and Business Media Deutschland GmbH, 2020. p. 343-358 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12369 LNCS).

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

TY - GEN

T1 - Learning Camera-Aware Noise Models

AU - Chang, Ke Chi

AU - Wang, Ren

AU - Lin, Hung Jin

AU - Liu, Yu Lun

AU - Chen, Chia Ping

AU - Chang, Yu Lin

AU - Chen, Hwann Tzong

PY - 2020

Y1 - 2020

N2 - Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/.

AB - Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/.

KW - Denoising

KW - GANs

KW - Noise model

KW - Sensor

UR - http://www.scopus.com/inward/record.url?scp=85097646885&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-58586-0_21

DO - 10.1007/978-3-030-58586-0_21

M3 - Conference contribution

AN - SCOPUS:85097646885

SN - 9783030585853

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 343

EP - 358

BT - Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings

A2 - Vedaldi, Andrea

A2 - Bischof, Horst

A2 - Brox, Thomas

A2 - Frahm, Jan-Michael

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th European Conference on Computer Vision, ECCV 2020

Y2 - 23 August 2020 through 28 August 2020

ER -

Chang KC, Wang R, Lin HJ, Liu YL, Chen CP, Chang YL et al. Learning Camera-Aware Noise Models. In Vedaldi A, Bischof H, Brox T, Frahm JM, editors, Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 343-358. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-58586-0_21

Learning Camera-Aware Noise Models

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this