Abstract
Image enhancement can be formulated as an optimization problem where one parameterized transformation function is used for enhancement purpose. The proper enhancement significantly depends on two factors- fine tuning of the parameters of the corresponding parameterized transformation function and other one is the selection of a proper objective function. In this study a parameterized variant of histogram equalization (HE) has been used for enhancement purpose and to tune the parameters of that variant a modified cuckoo search (CS) with new global and local search strategies is employed. This paper also concentrates on the selection of a proper objective function to preserve the original brightness of the image. A new objective function has been developed by combining fractal dimension (FD) and quality index based on local variance (QILV). Visual analysis and experimental results prove that modified CS with search strategies outperforms the traditional and some other existing modified CS algorithms. Considering the image’s brightness preserving capability, the proposed objective function significantly outperforms other existing objective functions.
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References
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. (Prentice Hall, New York, 2002).
S. D. Chen and A. R. Ramli, “Preserving brightness in histogram equalization based contrast enhancement techniques,” Digital Signal Processing 14, 413–428 (2004).
Y. T. Kim, “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. Consum. Electron. 43 (1), 1–8 (1997).
H. D. Cheng and X. J. Shi, “A simple and effective histogram equalization approach to image enhancement,” Digital Signal Processing 14, 158–170 (2004).
S. D. Chen and A. R. Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement,” IEEE Trans. Consumer Electron. 49, 1310–1319 (2003).
S. D. Chen and A. R. Ramli, “Contrast enhancement using recursive mean separated histogram equalization for scalable brightness preservation,” IEEE Trans. Consumer Electron. 49 (4), 1301–1309 (2003).
P. Shanmugavadivu, K. Balasubramanian, and A. Muruganandam, “Particle swarm optimized bihistogram equalization for contrast enhancement and brightness preservation of images,” Vis. Comput. 30, 387–399 (2014).
M. Abdullah-Al-Wadud, M. H. Kabir, M. A. A. Dewan, and O. Chae, “A dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consumer Electron. 53 (2), 593–600 (2007).
C. Zuo, Q. Chen, and X. Sui, “Range limited bi-histogram equalization for image contrast enhancement,” Optik 124, 425–431 (2013).
Q. Wang and R. K. Ward, “Fast image/video contrast enhancement based on weighted thresholded histogram equalization,” IEEE Trans. Consumer Electron. 53 (2), 757–764 (2007).
N. Sengee and H. K. Choi, “Brightness preserving weight clustering histogram equalization,” IEEE Trans. Consum. Electron. 54, 1329–1337 (2008).
M. Kim and M. G. Chung, “Recursively separated and weighted histogram equalization for brightness preser-vation and contrast enhancement,” IEEE Trans. Consum. Electron. 54, 1389–1397 (2008).
T. Kim and J. Paik, “Adaptive contrast enhancement using gain-controllable clipped histogram equalization,” IEEE Trans. Consumer Electron. 54 (4) (2008).
N. Otsu, “A threshold selection method from graylevel histograms,” IEEE Trans. Sys., Man., Cyber. 9 (1), 62–66 (1979).
S. K. Pal, D. Bhandari, and M. K. Kundu, “Genetic algorithms for optimal image enhancement,” Pattern Recogn. Lett. 15, 261–271 (1994).
S. Hashemi, S. Kiani, N. Noroozi, and M. E. Moghaddam, “An image contrast enhancement method based on genetic algorithm,” Pattern Recogn. Lett. 31, 1816–1824 (2010).
L. D. S. Coelho, J. G. Sauer, and M. Rudek, “Differential evolution optimization combined with chaotic sequences for image contrast enhancement,” Chaos, Solitons Fractals 42, 522–529 (2009).
K. G. Dhal, I. M. Quraishi, and S. Das, “Performance enhancement of differential evolution by incorporating Lévy flight and chaotic sequence for the cases of satellite images,” Int. J. Appl. Metaheuristic Comput. 6, 69–81 (2015).
A. Gorai and A. Ghosh, “Gray-level image enhancement by particle swarm optimization,” in Proc. World Congress on Nature and Biologically Inspired Computing (Coimbatore, 2009).
M. Barik, A. Sheta, and A. Ayesh, “Image enhancement using particle swarm optimization,” in Proc. World Congress on Engineering (London, 2007).
K. G. Dhal, I. M. Quraishi, and S. Das, “Development of firefly algorithm via chaotic sequence and population diversity to enhance the image contrast,” Natural Comput. 14, 1–12 (2015).
A. Bouaziz, A. Draa, and S. Chikhi, “Bat algorithm for fingerprint image enhancement,” in Proc. 12th Int. Symp. on Programming and Systems (ISPS) (Algiers, 2015), pp. 1–8.
K. G. Dhal, I. M. Quraishi, and S. Das, “Performance analysis of chaotic Lévy bat algorithm and chaotic cuckoo search algorithm for gray level image enhancement,” in Information Systems Design and Intelligent Applications (Springer, 2015), pp. 233–244.
T. R. Benala, S. D. Jampala, S. H. Villa, and B. Konathala, “A novel approach to image edge enhancement using artificial bee colony optimization algorithm for hybridized smoothening filters,” in Proc. World Congress on Nature and Biologically Inspired Computing (NaBIC 2009) (Coimbatore, 2009), pp. 1071–1076.
K. G. Dhal and S. Das, “Diversity conserved chaotic artificial bee colony algorithm based on brightness preserved histogram equalization and contrast stretching method,” Int. J. Nat. Comput. Res. 5, 45–73 (2015).
S. Agrawal and R. Panda, “An efficient algorithm for gray level image enhancement using cuckoo search,” in Proc. SEMCCO 2012 (Bhubaneswar, 2012), pp. 82–89.
A. K. Bhandaria, V. A. Sonia, A. Kumar, and G. K. Singh, “Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT–SVD,” ISA Trans. 53, 1286–1296 (2014).
S. Ghosh, S. Roy, U. Kumar, and A. Mallick, “Gray level image enhancement using cuckoo search algorithm,” in Advances in Intelligent System Computing (Springer, 2014), pp. 275–286.
K. G. Dhal, I. M. Quraishi, and S. Das, “A chaotic Lévy flight approach in bat and firefly algorithm for gray level image enhancement. I,” J. Image, Graph. Signal Processing 7, 69–76 (2015).
L. Maurya, P. K. Mahapatra, and G. Saini, “Modified cuckoo search-based image enhancement,” in Proc. 4th Int. Conf. on Frontiers in Intelligent Computing: Theory and Applications (FICTA) (Durgapur, 2015), pp. 625–635.
K. G. Dhal, I. M. Quraishi, and S. Das, “An improved cuckoo search based optimal ranged brightness preserved histogram equalization and contrast stretching method,” Int. J. Swarm Intellig. Res. 8, 1–29 (2017).
S. Walton, O. Hassan, K. Morgan, and M. R. Brown, “A review of the development and applications of the cuckoo search algorithm,” Swarm Intellig. Bio-Inspired Computation (2013). http://dx.doi.org/doi 10.1016/B978-0-12-405163-8.00011-9.10.1016/B978-0-12-405163-8.00011-9
X. S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” Int. J. Math. Modelling Num. Optimis. 1, 330–343 (2010).
O. S. Al-Kadi and D. Watson, “Texture analysis of aggressive and non-aggressive lung tumor CE CT images,” IEEE Trans. Biomed. Eng. 55, 1822–1830 (2008).
S. Aja-Fernández, R. S. JoséEstépar, C. Alberola-López, and C. F. Westin, “Image quality assessment based on local variance,” in Proc. 28th IEEE EMBS Annu. Int. Conf. (New York, 2006), pp. 4815–4818.
S. Aja-Fernández, R. S. JoséEstépar, and C. Alberola-López, “Full reference image quality assessment based on local statistics,” LPI Tech Rep. (Universidad de Valladolid, Jan. 2014), No. TECH-LPI2014-01.
J. Derrac, S. García, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm Evolut. Computat. 1 (1), 3–18 (2011).
X. Li and M. Yin, “Modified cuckoo search algorithm with self-adaptive parameter method,” Inf. Sci. (2014). http://dx.doi.org/. doi 10.1016/j.ins.2014.11.042
J. Kennedy, “Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance,” in Proc. 1999 Congress on Evolutionary Computation (Washington, 1999).
H. Wang, Z. Wu, and S. Rahnamayan, “Particle swarm optimisation with simple and efficient neighbourhood search strategies,” Int. J. Innovat. Comput. Appl. 3, 97–104 (2011).
S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential evolution using a neighborhood-based mutation operator,” IEEE Trans. Evolutionary Computat. 13, 526–553 (2009).
H. Wang, Z. Cui, H. Sun, S. Rahnamayan, and X. S. Yang, “Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism,” in Soft Computing (Springer, Berlin, Heidelberg, 2016), pp. 1–15. doi 10.1007/s00500-016-2116-z
C. S. D. Leandro and C. M. Viviana, “A novel particle swarm optimization approach using Henon map and implicit filtering local search for economic load dispatch,” Chaos, Solit. Fractals 39, 510–518 (2009).
R. Sheikholeslami and A. Kaveh, “A survey of chaos embedded meta-heuristic algorithms,” Int. J. Opt. Civil. Eng. 3 (4), 617–633 (2013).
L. D. S. Coelho and V. C. Mariani, “Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization,” Expert Syst. Appl. 34, 1905–1913 (2008).
A. R. Jordehi, “A chaotic-based big bang–big crunch algorithm for solving global optimisation problems,” Neural Comput. Appl. 25, 1329–1335 (2014).
C. Choi and J. J. Lee, “Chaotic local search algorithm,” Artif. Life Robotics 2, 41–47 (1998).
J. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon, and A. Abraham, “Inertia weight strategies in particle swarm optimization,” in Proc 3rd World Congress on Nature and Biologically Inspired Computing (Salamanca, 2011), pp. 640–647.
R. Caponetto, L. Fortuna, S. Fazzino, and M. G. Xibilia, “Chaotic sequences to improve the performance of evolutionary algorithms,” IEEE Trans. Evolut. Comput. 7, 289–304 (2003).
M. Jamil and H. J. Zepernick, “Lévy flights and global optimization,” Bio-Inspired Comput. (2013). http://dx.doi.org/doi 10.1016/B978-0-12-405163-8.00003-X.10.1016/B978-0-12-405163-8.00003-X
X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Trans. Evol. Comput. 3, 82–102 (1999).
C. L. Chien and D. C. Tseng, “Color image enhancement with exact HIS color model,” Int. J. Innovat. Comput., Inf. Control 7, 6691–6710 (2011).
C. L. Chien and W. H. Tsai, “Image fusion with no gamut problem by improved nonlinear HIS transforms for remote sensing,” IEEE Trans. Geosci. Remote Sensing 52, 651–663 (2014).
C. Yim and A. C. Bovik, “Quality assessment of deblocked images,” IEEE Trans. Image Processing 20, 88–98 (2011).
R. Wang, Y. Zhou, C. Zhao, and H. Wu, “A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation,” Bio-Med. Mater. Eng. 26, 1345–S351 (2015).
J. Derrac, S. Garcia, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm Evolut. Comput. 1, 3–18 (2011).
C. Gatta, A. Rizzi, and D. Marini, “ACE: an automatic color equalization algorithm,” in Proc. 1st European Conf. on Color in Graphics Image and Vision (CGIV02) (Poitiers, 2002).
S. K. Naik and C. A. Murthy, “Hue preserving color image enhancement without gamut problem,” IEEE Trans. Image Processing 12, 1591–1598 (2003).
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Krishna Gopal Dhal completed his B.Tech and M. Tech from Kalyani Government Engineering College. Currently he is working as Assistant Professor in Dept. of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India. His research interests are image processing and nature inspired metaheuristics.
Sanjoy Das completed his B.E. from Regional Engineering College, Durgapur, M.E. from Bengal Engineering College (Deemed Univ.), Howrah, Ph.D. from Bengal Engineering and Science University, Shibpur. Currently he is working as Associate Professor in Dept. of Engineering and Technological Studies, University of Kalyani, Nadia, West Bengal, India. His research interests are tribology and optimization techniques.
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Dhal, K.G., Das, S. Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement. Pattern Recognit. Image Anal. 27, 695–712 (2017). https://doi.org/10.1134/S1054661817040046
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DOI: https://doi.org/10.1134/S1054661817040046