Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

doi:10.21629/JSEE.2018.05.18

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (5): 1079-1088.doi: 10.21629/JSEE.2018.05.18

• Software Algorithm and Simulation • Previous Articles Next Articles

Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

Baoping WANG¹(), Jianjun MA^1,²(), Zhaoxuan HAN^1,²(), Yan ZHANG^1,²(), Yang FANG^1,^2,*(), Yimeng GE^1,²()

¹ National Key Laboratory of Science and Technology on UAV, Northwestern Polytechnical University, Xi’an 710065, China
² School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Received:2017-05-12 Online:2018-10-26 Published:2018-11-14
Contact: Yang FANG E-mail:wbpluo@sina.com;majianjun133@foxmail.com;zhaoxuan_han@foxmail.com;shuxiangqiuyue@163.com;fang_yang122@vip.sina.com;ge0415@gmail.com
About author:WANG Baoping was born in 1964. He received his Ph.D. degree from Department of Electronic Engineering, Xidian University, in 2004. He is currently a professor and a master instructor in National Key Laboratory of Science and Technology on UAV. So far he has published more than 30 papers, in which four papers were SCI indexed and ten papers were EI indexed. His current research interests include imaging processing and radar imaging. E-mail: wbpluo@sina.com|MA Jianjun was born in 1993. He received his B.S. degree in computer science and technology from Shijiazhuang TieDao University, in 2016. He is currently working toward his M.S. degree in School of Electronics and Information, Northwestern Polytechnical University. His current research interests include radar imaging and image processing. E-mail: majianjun133@foxmail.com|HAN Zhaoxuan was born in 1993. He is currently a master student in Department of Electronics and Information, Northwestern Polytechnical University. His research interests include SAR imaging and imaging processing. E-mail: zhaoxuan_han@foxmail.com|ZHANG Yan was born in 1991. He received his M.S. degree from School of Electronics and Information, Northwestern Polytechincal University (NWPU). He is currently a Ph.D. student in School of Electronics and Information, NWPU. His research interests are imaging processing and radar imaging. E-mail: shuxiangqiuyue@163.com|FANG Yang was born in 1988. He received his M.S. degree from School of Electronics and Information, Northwestern Polytechnical University (NWPU). He is currently a Ph.D. student in School of Electronics and Information, NWPU. He also is a student member of IEEE. So far he has published 10 papers, in which three papers were SCI indexed and six papers was EI indexed. His current research interests include radar imaging, imaging processing and telemetry antenna. E-mail: fang_yang122@vip.sina.com|GE Yimeng was born in 1993. She received her B.S. degree in computer science and technology from Xi’an University of Posts & Telecommunications, in 2015, where she is currently working toward her M.S. degree in computer application technology. Her research interests include image processing and machine learning. E-mail: ge0415@gmail.com
Supported by:
the National Natural Science Foundation of China(61472324);This work was supported by the National Natural Science Foundation of China (61472324)

Abstract

Abstract:

To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing (LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics. To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter α according to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed. The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.

Key words: image enhancement, fuzzy entropy, fuzzy partition, logarithmic image processing (LIP) model, human visual characteristic, statistical characteristic

Baoping WANG, Jianjun MA, Zhaoxuan HAN, Yan ZHANG, Yang FANG, Yimeng GE. Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 1079-1088.

Figures/Tables 13

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References 15

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n	Bright region						n	Dark region
n	$\overline f $	$\overline {{f}'} $	$f$	${f}'$	$\Delta f_{12} $	$\Delta {f}'_{12} $	n	$\overline f $	$\overline {{f}'} $	$f$	${f}'$	$\Delta f_{34} $	$\Delta {f}'_{34} $
1	0	0	1	1	0	0	3	0	0	1	1	0	0
1	0	0	0	0	4	2	3	6	4	4	6	4	6
2	0	0	0	0			4	1	1	0	0
2	4	2	6	8			4	0	0	0	0

n	Bright region						n	Dark region
n	$\overline f $	$\overline {{f}'} $	$f$	${f}'$	$\Delta f_{12} $	$\Delta {f}'_{12} $	n	$\overline f $	$\overline {{f}'} $	$f$	${f}'$	$\Delta f_{34} $	$\Delta {f}'_{34} $
1	0	0	1	1	0	0	3	0	0	1	1	0	0
1	0	0	0	0	1	5	3	7	9	3	1	3	1
2	0	0	0	0			4	1	1	0	0
2	1	5	9	5			4	0	0	0	0

n	Bright region						n	Middle region						n	Dark region
n	$\overline f $	$\overline {{f}'} $	$f$	${f}'$	$\Delta f_{12} $	$\Delta {f}'_{12} $	n	$\overline f $	$\overline {{f}'} $	$f$	${f}'$	$\Delta f_{34} $	$\Delta {f}'_{34} $	n	$\overline f $	$\overline {{f}'} $	$f$	${f}'$	$\Delta f_{56} $	$\Delta {f}'_{56} $
1	0	0	1	1	0	0	3	0	0	0	0	0	0	5	0	0	0	0	0	0
1	0	0	0	0	4	2	3	4	3	6	7	2	4	5	6	7	4	3	4	3
2	0	0	0	0			4	0	0	0	0			6	1	1	0	0
2	4	3	6	7			4	6	7	4	3			6	0	0	0	0

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Adaptive image enhancement algorithm based on fuzzy entropy and human visual characteristics

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