Night Time Image Enhancement by Improved Nonlinear Model

Yao Zhang; Chenxu Wang; Xinsheng Wang; Jing Wang; Le Man

doi:10.1007/978-3-319-73447-7_34

Night Time Image Enhancement by Improved Nonlinear Model

Yao Zhang
, Chenxu Wang^*
, Xinsheng Wang
, Jing Wang
, Le Man

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

Low light or poor shooting angle and other issues often make the camera to take night time images and affect the naked-eye observation or computer identification, so it is important to enhance the lightness of night time image. Although the existing non-linear luminance enhancement method can improve the brightness of the low light area, the excessive promotion led to high light area distortion. Based on the existing image luminance processing algorithm, we proposed an adaptive night time image improving method in the basis of nonlinear brightness enhancement model is proposed to process the segmentations of image brightness by using the logarithmic function. The segmentation threshold is determined by the Otsu, and the adjustment factor of the backlight region in the transfer function is calculated from the area ratio of the backlight area. The conclusion comes from the simulation. The method involves improving the image quality and ensuring that the entire picture is natural without distortion. In the meanwhile, the processing speed is not much slower compared with the existing processing algorithms.

Original language	English
Title of host publication	Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings
Editors	Bo Li, Xuemai Gu, Gongliang Liu
Publisher	Springer Verlag
Pages	304-315
Number of pages	12
ISBN (Print)	9783319734460
DOIs	https://doi.org/10.1007/978-3-319-73447-7_34
State	Published - 2018
Externally published	Yes
Event	2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017 - Weihai, China Duration: 5 Aug 2017 → 6 Aug 2017

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	227 LNICST
ISSN (Print)	1867-8211

Conference

Conference	2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017
Country/Territory	China
City	Weihai
Period	5/08/17 → 6/08/17

Keywords

Adaptive Nonlinearity Model
Brightness
Night time images
Otsu Threshold

Access to Document

10.1007/978-3-319-73447-7_34

Cite this

Zhang, Y., Wang, C., Wang, X., Wang, J., & Man, L. (2018). Night Time Image Enhancement by Improved Nonlinear Model. In B. Li, X. Gu, & G. Liu (Eds.), Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings (pp. 304-315). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 227 LNICST). Springer Verlag. https://doi.org/10.1007/978-3-319-73447-7_34

Zhang, Yao ; Wang, Chenxu ; Wang, Xinsheng et al. / Night Time Image Enhancement by Improved Nonlinear Model. Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. editor / Bo Li ; Xuemai Gu ; Gongliang Liu. Springer Verlag, 2018. pp. 304-315 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{f0de111027c94ee19bf755c363059249,

title = "Night Time Image Enhancement by Improved Nonlinear Model",

abstract = "Low light or poor shooting angle and other issues often make the camera to take night time images and affect the naked-eye observation or computer identification, so it is important to enhance the lightness of night time image. Although the existing non-linear luminance enhancement method can improve the brightness of the low light area, the excessive promotion led to high light area distortion. Based on the existing image luminance processing algorithm, we proposed an adaptive night time image improving method in the basis of nonlinear brightness enhancement model is proposed to process the segmentations of image brightness by using the logarithmic function. The segmentation threshold is determined by the Otsu, and the adjustment factor of the backlight region in the transfer function is calculated from the area ratio of the backlight area. The conclusion comes from the simulation. The method involves improving the image quality and ensuring that the entire picture is natural without distortion. In the meanwhile, the processing speed is not much slower compared with the existing processing algorithms.",

keywords = "Adaptive Nonlinearity Model, Brightness, Night time images, Otsu Threshold",

author = "Yao Zhang and Chenxu Wang and Xinsheng Wang and Jing Wang and Le Man",

note = "Publisher Copyright: {\textcopyright} 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017 ; Conference date: 05-08-2017 Through 06-08-2017",

year = "2018",

doi = "10.1007/978-3-319-73447-7\_34",

language = "英语",

isbn = "9783319734460",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Verlag",

pages = "304--315",

editor = "Bo Li and Xuemai Gu and Gongliang Liu",

booktitle = "Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings",

address = "德国",

}

Zhang, Y, Wang, C , Wang, X, Wang, J & Man, L 2018, Night Time Image Enhancement by Improved Nonlinear Model. in B Li, X Gu & G Liu (eds), Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 227 LNICST, Springer Verlag, pp. 304-315, 2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017, Weihai, China, 5/08/17. https://doi.org/10.1007/978-3-319-73447-7_34

Night Time Image Enhancement by Improved Nonlinear Model. / Zhang, Yao; Wang, Chenxu ; Wang, Xinsheng et al.
Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. ed. / Bo Li; Xuemai Gu; Gongliang Liu. Springer Verlag, 2018. p. 304-315 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 227 LNICST).

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

TY - GEN

T1 - Night Time Image Enhancement by Improved Nonlinear Model

AU - Zhang, Yao

AU - Wang, Chenxu

AU - Wang, Xinsheng

AU - Wang, Jing

AU - Man, Le

PY - 2018

Y1 - 2018

N2 - Low light or poor shooting angle and other issues often make the camera to take night time images and affect the naked-eye observation or computer identification, so it is important to enhance the lightness of night time image. Although the existing non-linear luminance enhancement method can improve the brightness of the low light area, the excessive promotion led to high light area distortion. Based on the existing image luminance processing algorithm, we proposed an adaptive night time image improving method in the basis of nonlinear brightness enhancement model is proposed to process the segmentations of image brightness by using the logarithmic function. The segmentation threshold is determined by the Otsu, and the adjustment factor of the backlight region in the transfer function is calculated from the area ratio of the backlight area. The conclusion comes from the simulation. The method involves improving the image quality and ensuring that the entire picture is natural without distortion. In the meanwhile, the processing speed is not much slower compared with the existing processing algorithms.

AB - Low light or poor shooting angle and other issues often make the camera to take night time images and affect the naked-eye observation or computer identification, so it is important to enhance the lightness of night time image. Although the existing non-linear luminance enhancement method can improve the brightness of the low light area, the excessive promotion led to high light area distortion. Based on the existing image luminance processing algorithm, we proposed an adaptive night time image improving method in the basis of nonlinear brightness enhancement model is proposed to process the segmentations of image brightness by using the logarithmic function. The segmentation threshold is determined by the Otsu, and the adjustment factor of the backlight region in the transfer function is calculated from the area ratio of the backlight area. The conclusion comes from the simulation. The method involves improving the image quality and ensuring that the entire picture is natural without distortion. In the meanwhile, the processing speed is not much slower compared with the existing processing algorithms.

KW - Adaptive Nonlinearity Model

KW - Brightness

KW - Night time images

KW - Otsu Threshold

UR - https://www.scopus.com/pages/publications/85045275945

U2 - 10.1007/978-3-319-73447-7_34

DO - 10.1007/978-3-319-73447-7_34

M3 - 会议稿件

AN - SCOPUS:85045275945

SN - 9783319734460

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 304

EP - 315

BT - Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings

A2 - Li, Bo

A2 - Gu, Xuemai

A2 - Liu, Gongliang

PB - Springer Verlag

T2 - 2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017

Y2 - 5 August 2017 through 6 August 2017

ER -

Zhang Y, Wang C , Wang X, Wang J, Man L. Night Time Image Enhancement by Improved Nonlinear Model. In Li B, Gu X, Liu G, editors, Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. Springer Verlag. 2018. p. 304-315. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-319-73447-7_34

Night Time Image Enhancement by Improved Nonlinear Model

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Keywords

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