Image Backlight Compensation Using Recurrent Functional Neural Fuzzy Networks Based on Modified Differential Evolution

Document Type: Research Paper


Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, ROC


In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.
The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.
The proposed RFNFN is based on the two backlight factors that can accurately detect the compensation degree. According to the backlight level, the compensation curve function of a backlight image can be adaptively adjusted. In our experiments, six backlight images are used to verify the performance of proposed method.
Experimental results demonstrate that the proposed method performs well in backlight problems.


[1] C. H. Chen, C. J. Lin and C. T. Lin, A recurrent functional-link-based neural fuzzy system
and its applications, Proceedings of the 2007 IEEE Symposium on Computational Intelligence
in Image and Signal Processing (CIISP 2007), (2007), 415-420.
[2] J. Duan and G. Qiu, Novel histogram processing for colour image enhancement, Proceedings
of the Third International Conference on Image and Graphics (ICIG04), Hong Kong, China,
(2004), 55-58.
[3] A. A. Fahmy and A. M. Abdel Ghany, Adaptive functional-based neuro-fuzzy PID incremental
controller structure, Neural Computing and Applications, 26(6) (2015), 1423-1438.
[4] M. Hojati and S. Gazor, Hybrid adaptive fuzzy identi cation and control of nonlinear systems,
IEEE Transactions on Fuzzy Systems, 10(2) (2002), 198-210.
[5] T. H. Huang, K. T. Shih, S. L. Yeh and H. H. Chen, Enhancement of backlight-scaled images,
IEEE Transactions on Image Processing, 22(12) (2013), 4587-4597.
[6] H. Kabir, A. Al-Wadud and O. Chae, Brightness preserving image contrast enhancement
using weighted mixture of global and local transformation functions, The International Arab
Journal of Information Technology, 7(4) (2010), 403-410.
[7] H. Y. Lin, C. Y. Lin, C. J. Lin, S. C. Yang and C. Y. Yu, A study of digital image enlargement
and enhancement, Mathematical Problems in Engineering, Article ID 825169, (2014).
[8] D. Menotti, L. Najman, J. Facon and A. A. A. de Araujo, Multi-histogram equalization meth-
ods for contrast enhancement and brightness preserving, IEEE Transactions on Consumer
Electronics, 53(3) (2007), 1186-1194.
[9] A. H. Mohamed, A genetic based neuro-fuzzy controller system, International Journal of
Computer Applications, 94(1) (2014), 14-17.
[10] M. Panella and A. S. Gallo, An input-output clustering approach to the synthesis of ANFIS
networks, IEEE Transaction on Fuzzy Systems, 13(1) (2005), 69-81.
[11] O. Patel, Y. P. S. Maravi and S. Sharma, A comparative study of histogram equalization
based image enhancement techniques for brightness preservation and contrast enhancement,
Signal & Image Processing: An International Journal (SIPIJ), 4(5) (2013), 11-25.
[12] T. K. S. Paterlini, Di erential evolution and particle swarm optimization in partitional clus-
tering, Computational Statics & Data Analysis, 50(5) (2006), 1220-1247.
[13] A. P. Piotrowski, Di erential evolution algorithms applied to neural network training su er
from stagnation, Applied Soft Computing, 21(2014), 382V406.
[14] R. Storn and K. Price, Di erential evolution-A simple and ecient heuristic for global op-
timization over continuous spaces, Journal of Global Optimization, 11(4) (1997), 341-359.
[15] M. A.Wadudx, M. H. Kabir, M. A. A. Dewan and O. Chae, A dynamic histogram equalization
for image contrast enhancement, IEEE Transactions on Consumer Electronics, 53(2) (2007),
[16] J. Yen and R. Langari, Fuzzy Logic: intelligence, control, and information, Prentice Hall,
[17] C. Y. Yu, H. Y. Lin and R. N. Lin, Eight-scale image contrast enhancement based on adaptive
inverse hyperbolic, International Symposium on Computer, Consumer and Control, Taichung,
Taiwan, (2014), 98-102.
[18] C. Y. Yu, H. Y. Lin, Y. C. Ouyang and T. W. Yu, Modulated AIHT image contrast en-
hancement algorithm based on contrast-limited adaptive histogram equalization, International
Journal on Applied Mathematics and Information Sciences, 7(2) (2013), 449-454.
[19] C. Y. Yu, Y. C. Ouyang, C. M. Wang and C. I. Chang, Adaptive inverse hyperbolic tan-
gent algorithm for dynamic contrast adjustment in displaying scenes, EURASIP Journal on
Advances in Signal Processing, 485151 (2010), 1-20.
[20] J. Yue, J. Liu, X. Liu and W. Tan, Identi cation of nonlinear system based on ANFIS with
subtractive clustering, The Sixth World Congress on Intelligent Control and Automation
(WCICA 2006), 2006, 1852-1856.
[21] K. Zuiderveld, Contrast limited adaptive histogram equalization, In: P. Heckbert: Graphics
Gems IV, Academic Press 1994