Abstract
In this contemporary era, technological dependencies on digital images are indispensable. Quality enhancement is an obligatory part of image pre-processing, so that desired information can be harvested efficiently. The varying texture in any image contributes to the information about the structural arrangement of surface content of the captured scene. Fractional-order calculus (FOC) and its related optimally ordered adaptive filtering are quite appreciable. Especially for texture preserved image quality enhancement, analytical strength of FOC is latently too valuable to be casually dismissed. No any closed-form free-lunch theory survives for evaluating the required fractional-order for overall quality enhancement because non-linear features of images from diverse domains require highly adaptive on-demand processing. Hence, texture preserved image quality enhancement can be considered as an NP-hard problem, where there isn’t an exact solution that runs in polynomial time. Thus, by the virtue of evolutionary algorithms along with their associated swarm intelligence, a near-exact solution can be attained. Memetic hybridization of cuckoo search optimizer (CSO) and sine-cosine optimizer (SCO) for this purpose is the core contribution in this chapter. In this chapter, to support the theoretical discussion in the context of the fundamentals behind CSO and SCO, their mathematical beauty of convergence is also highlighted which itself has resulted from the balance exploration and exploitation behavior. A novel texture-dependent objective function is also proposed in this chapter for imparting the patch-wise overall texture preserved image quality enhancement. Finally, the comparative analysis of results illustrates the superior capability of the proposed approach.
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References
Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56(4), 2475–2480 (2010)
Singh, H., Kumar, A., Balyan, L.K., Lee, H.: Fuzzified histogram equalization based gamma corrected cosine transformed energy redistribution for image enhancement. In: 23rd IEEE International Conference on Digital Signal Processing (DSP), Shanghai, China, pp. 1–5 (2018)
Singh, K., Kapoor, R.: Image enhancement via median mean based sub image clipped histogram equalization. Optik Int. J. Light Electr. Optics. 125(17), 4646–4651 (2014)
Singh, K., Kapoor, R.: Image enhancement using exposure based sub image histogram equalization. Pattern Recogn. Lett. 36, 10–14 (2014)
Huang, S.C., Cheng, F.C., Chiu, Y.S.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. 22(3), 1032–1041 (2013)
Singh, H., Kumar, A.: Satellite image enhancement using beta wavelet based gamma corrected adaptive knee transformation. In: 5th IEEE International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India, pp. 128–132 (2016)
Singh, H., Agrawal, N., Kumar, A., Singh, G.K., Lee, H.N.: A novel gamma correction approach using optimally clipped sub-equalization for dark image enhancement. In: 21st IEEE International Conference on Digital Signal Processing (DSP), Beijing, China, pp. 497–501 (2016)
Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: A novel optimally gamma corrected intensity span maximization approach for dark image enhancement. In: 22nd IEEE International Conference on Digital Signal Processing (DSP), London, United Kingdom, pp. 1–5 (2017)
Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Regionally equalized and contextually clipped gamma correction approach for dark image enhancement. In: 4th IEEE International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, pp. 431–436 (2017)
Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Dark image enhancement using optimally compressed and equalized profile based parallel gamma correction. In: 6th IEEE International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 1299–1303 (2017)
Fu, X., Wang, J., Zeng, D., Huang, Y., Ding, X.: Remote sensing image enhancement using regularized histogram equalization and DCT. IEEE Geosci. Remote Sens. Lett. 12(11), 2301–2305 (2015)
Lin, S.C.F., Wong, C.Y., Rahman, M.A., Jiang, G., Liu, S., Kwok, N.: Image enhancement using the averaging histogram equalization approach for contrast improvement and brightness preservation. Comput. Electr. Eng. 46, 356–370 (2014)
Wong, C.Y., Jiang, G., Rahman, M.A., Liu, S., Lin, S.C.F., Kwok, N., et al.: Histogram equalization and optimal profile compression based approach for color image enhancement. J. Vis. Commun. Image Represent. 38, 802–813 (2016)
Wong, C.Y., Liu, S., Liu, S.C., Rahman, M.A., Lin, S.C.F., Jiang, G., et.al.: Image contrast enhancement using histogram equalization with maximum intensity coverage. J. Modern Opt. 63(16), 1618–1629
Lin, S.C.F., Wong, C.Y., Jiang, G., Rahman, M.A., Ren, T.R., Kwok, N., et al.: Intensity and edge based adaptive unsharp masking filter for color image enhancement. Optik Int. J. Light Electr. Opt. 127(1), 407–414 (2016)
Singh, K., Vishwakarma, D.K., Walia, G.S., Kapoor, R.: Contrast enhancement via texture region based histogram equalization. J. Mod. Opt. 63(15), 1444–1450 (2016)
Singh, H., Kumar, A., Balyan, L.K., Lee, H.N.: Optimally sectioned and successively reconstructed histogram sub-equalization based gamma correction for satellite image enhancement. Multimed. Tools Appl. 78(14), 20431–20463 (2019)
Hemanth, J., Balas, V.E.: Nature Inspired Optimization Techniques for Image Processing Applications. Springer International Publishing, Berlin (2019)
Singh, H., Kumar, A., Balyan, L.K., Lee, H.N.: Fractional order integration based fusion model for piecewise gamma correction along with textural improvement for satellite images. IEEE Access 7, 37192–37210 (2019)
Singh, H., Kumar, A., Balyan, L.K., Lee, H.N.: Piecewise gamma corrected optimally framed Grumwald-Letnikov fractional differential masking for satellite image enhancement. In: 7th IEEE International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 0129–0133 (2018)
Mohan, S., Mahesh, T.R.: Particle swarm optimization based contrast limited enhancement for mammogram images. In: International Conference on Intelligent Systems and Control, pp. 384–388 (2013)
Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Slantlet filter-bank based satellite image enhancement using gamma corrected knee transformation. Int. J. Electron. 105(10), 1695–1715 (2018)
Shanmugavadivu, P., Balasubramanian, K., Muruganandam, A.: Particle swarm optimized bi-histogram equalization for contrast enhancement and brightness preservation of images. J. Vis. Comput. 30(4), 387–399 (2014)
Wan, M., Gu, G., Qian, W., Ren, K., Chen, Q., Maldague, X.: Particle swarm optimization based local entropy weighted histogram equalization for infrared image enhancement. Infrared Phys. Technol. 91, 164–181 (2018)
Babu, P., Rajamani, V.: Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images. Int. J. Imaging Syst. Technol. 25(1), 24–32 (2015)
Dhal, K.G., Das, S.: Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement. J. Pattern Recognit. Image Anal. 27(4), 695–712 (2017)
Dhal, K.G., Das, S.: Local search based dynamically adapted Bat Algorithm in image enhancement domain. Int. J. Comput. Sci. Math. (2017)
Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement. Comput. Electr. Eng. 70, 462–475 (2018)
Joshi, P., Prakash, S.: An efficient technique for image contrast enhancement using artificial bee colony. In: International Conference on Identity, Security and Behavior Analysis, pp. 1–6 (2015)
Chen, J., Yu, W., Tian, J., Chen, L., Zhou, Z.: Image contrast enhancement using an artificial bee colony algorithm. J. Swarm Evolut. Comput. 38, 287–294 (2017)
Hoseini, P., Shayesteh, M.G.: Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing. J. Digit. Signal Process. 23(3), 879–893 (2013)
Verma, O.P., Chopra, R.R., Gupta, A.: An adaptive bacterial foraging algorithm for color image enhancement. In: Annual Conference on Information Science and Systems, pp. 1–6 (2016)
Singh, H., Kumar, A., Balyan, L.K., Singh, G.K.: A new optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement. Comput. Electr. Eng. 75, 245–261 (2019)
Singh, H., Kumar, A., Balyan, L.K.: A levy flight firefly optimizer based piecewise gamma corrected unsharp masking framework for satellite image enhancement. In: 14th IEEE India Council International Conference (INDICON), Roorkee, India, pp. 1–6 (2017)
Singh, H., Kumar, A., Balyan, L.K.: Cuckoo search optimizer based piecewise gamma corrected auto clipped tile wise equalization for satellite image enhancement. In: 14th IEEE India Council International Conference (INDICON), Roorkee, India, pp. 1–5 (2017)
Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)
Singh, H., Kumar, A., Balyan, L.K.: A sine-cosine optimizer-based gamma corrected adaptive fractional differential masking for satellite image enhancement. In: Harmony Search and Nature Inspired Optimization Algorithms. Advances in Intelligent Systems and Computing, vol. 741, pp. 633–645. Springer, Singapore (2019)
Tawhid, M.A., Savsani, V.: Multi objective sine cosine algorithm for multi objective engineering design problems. J. Neural Comput. Appl. 31(2), 915–929 (2019)
Hafez, A.I., Zawbaa, H.M., Emary, E., Hassanien, A.E.: Sine cosine optimization algorithm for feature selection. In: International Symposium on Innovations in Intelligent Systems and Applications, pp. 1–5 (2016)
Reddy, K.S., Panwar, L.K., Panigrahi, B.K., Kumar, R.: A new binary variant of sine cosine algorithm: development and application to solve profit based unit commitment problem. Arab J. Sci. Eng. 43, 4041–4056 (2018)
Elaziz, M.A., Oliva, D., Xiong, S.: An improved opposition based sine cosine algorithm for global optimization. J. Exp. Syst. Appl. 90, 484–500 (2017)
Ning, L., Gang, L., Liang, D.Z.: An improved sine cosine algorithm based on levy flight. Int. Conf. Dig. Image Proces. 10420(104204R), 1–6 (2017)
Qu, C., Zeng, Z., Dai, J., Yi, Z., He, W.: A modified sine cosine algorithm based on neighborhood search and greedy levy mutation. J. Comput. Intell. Neurosci. 2, 1–19 (2018)
Meshkat, M., Parhizgar, M.: A novel weighted update position mechanism to improve the performance of sine cosine algorithm. In: Conference of Iranian Joint Congress on Fuzzy and Intelligent Systems, pp. 166–171 (2017)
Nayak, D.R., Dash, R., Majhi, B., Wang, S.: Combining extreme learning machine with modified sine cosine algorithm for detection of pathological brain. J. Comput. Electr. Eng. 68, 366–380 (2018)
Chen, K., Zhou, F., Yin, L., Wang, S., Wang, Y., Wan, S.: A hybrid particle swarm optimizer with sine cosine acceleration coefficients. J. Inform. Sci. 422, 218–241 (2018)
Gupta, S., Deep, K.: Improved sine cosine algorithm with crossover scheme for global optimization. J. Knowl. Based Syst. 165, 374–406 (2019)
Gupta, S., Deep, K.: A hybrid self-adaptive sine cosine algorithm with opposition based learning. J. Expert Syst. Appl. 119, 210–230 (2019)
Fernandez, A., Pena, A., Valenzuela, M., Pinto, H.: A binary percentile sine cosine optimization algorithm applied to the set covering problem. J. Comput. Stat. Methods Intell. Syst. 859, 285–295 (2019)
Yang, X.S., Deb, S.: Cuckoo search via Lévy-flights. In: Proceedings of World Congress Nature Biology Inspired Computing, pp. 210–214 (Dec. 2009)
Zhou, Y., Zheng, H., Luo, Q., Wu, J.: An improved cuckoo search algorithm for solving planar graph coloring problem. Int. J. Appl. Math. Inf. Sci. 7(2), 785–792 (2013)
Walton, S., Hassan, O., Morgan, K., Brown, M.R.: Modified cuckoo search: a new gradient free optimization algorithm. J. Chaos, Solitons Fractals 44, 710–718 (2011)
Tuba, M., Subotic, M., Stanarevic, N.: Performance of a modified cuckoo search algorithm for unconstrained optimization problems. WSEAS Trans. Syst. 11(2), 62–74 (2012)
Aziz, M.A.E., Hassanien, A.E.: Modified cuckoo search algorithm with rough sets for feature selection. J. Neural Comput. Appl. 29, 925–934 (2018)
Giridhar, M.S., Sivanagaraju, S., Suresh, C.V., Reddy, P.U.: Analyzing the multi objective analytical aspects of distribution systems with multiple multi-type compensators using modified cuckoo search algorithm. Int. J. Parallel Emergent Distrib. Syst. 32(6), 549–571 (2017)
Tawfik, A.S., Badr, A.A., Rahman, I.F.A.: One rank cuckoo search algorithm with application to algorithmic trading systems optimization. Int. J. Comput. Appl. 64(6), 30–37 (2013)
Nguyen, T.T., Vo, D.N., Dinh, B.H.: Cuckoo search algorithm using different distributions for short term hydrothermal scheduling with reservoir volume constraint. Int. J. Electr. Eng. Inf. 8(1), 76–92 (2016)
Ouaarab, A., Ahiod, B., Yang, X.S.: Discrete cuckoo search algorithm for the travelling salesman problem. J. Neural Comput. Appl. 24, 1659–1669 (2014)
Gherboudj, A., Layeb, A., Chikhi, S.: Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm. Int. J. Bio Inspir. Comput. 4(4), 229–236 (2012)
J.H. Lin, I.H. Lee, Emotional chaotic cuckoo search for the reconstruction of chaotic dynamics, Conference of Latest Advances in Systems Science and Computational Intelligence, 123–128 (2012)
Nawi, N.M., Khan, A., Rehman, M.Z.: A new cuckoo search based levenberg-marquardt algorithm. In: International Conference on Computational Science and its Applications, pp. 438–451 (2013)
Zhou, Y., Zheng, H.: A new complex valued cuckoo search algorithm. Sci. World J. 13(1) (2013)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (2006)
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Singh, H., Kumar, A., Balyan, L.K., Lee, H.N. (2020). Texture-Dependent Optimal Fractional-Order Framework for Image Quality Enhancement Through Memetic Inclusions in Cuckoo Search and Sine-Cosine Algorithms. In: Hemanth, D., Kumar, B., Manavalan, G. (eds) Recent Advances on Memetic Algorithms and its Applications in Image Processing. Studies in Computational Intelligence, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-15-1362-6_2
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