Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)

A self-adaptive Contrast Enhancement Method Based on Gradient and Intensity Histogram for Remote Sensing Images

Authors
Tieqiao Chen, Jiahang Liu, Xiuqin Su, Jia Liu, Feng Zhu, Yihao Wang
Corresponding Author
Tieqiao Chen
Available Online July 2016.
DOI
https://doi.org/10.2991/iccia-17.2017.53How to use a DOI?
Keywords
Contrast enhancement, remote sensing image, self-adaptive algorithm, gradient and intensity histogram.
Abstract
This paper proposes an efficient method to modify gradient and intensity histograms (GIH) for contrast enhancement, which plays an important role in remote sensing image processing and information extraction. First, a self-adaptive algorithm is used to flatten the shape of GIH of input image according to standard deviation of GIH. Then, the standard lookup table-based histogram equalization procedure is applied to get well enhanced image. Experimental results, using various remote sensing images, show that the proposed method generates enhanced images with more information and higher visual quality, compared with several conventional methods.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
Part of series
Advances in Computer Science Research
Publication Date
July 2016
ISBN
978-94-6252-361-6
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccia-17.2017.53How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Tieqiao Chen
AU  - Jiahang Liu
AU  - Xiuqin Su
AU  - Jia Liu
AU  - Feng Zhu
AU  - Yihao Wang
PY  - 2016/07
DA  - 2016/07
TI  - A self-adaptive Contrast Enhancement Method Based on Gradient and Intensity Histogram for Remote Sensing Images
BT  - 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 312
EP  - 316
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.53
DO  - https://doi.org/10.2991/iccia-17.2017.53
ID  - Chen2016/07
ER  -