Abstract
Artificial immune system (AIS) inspired by immune system of vertebrates can be used for solving optimization problem. In this paper, image enhancement is considered as a problem of optimization and AIS is used to solve and find the optimal solution of this problem. Here, image enhancement is done by enhancing the pixel intensities of the images through a parameterized transformation function. The main task is to achieve the best enhanced image with the help of AIS by optimizing the parameters. The results have proved better when compared with other standard enhancement techniques like Histogram equalization (HE) and Linear Contrast Stretching (LCS).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aickelin, U., Qi, C.: On affinity measures for artificial immune system movie recommenders. Paper presented at the The 5th International Conference on: Recent Advances in Soft Computing, Nottingham, UK (2004)
Bedi, S., Khandelwal, R.: Various Image Enhancement Techniques-A Critical Review. International Journal of Advanced Research in Computer and Communication Engineering 2(3), 1605–1609 (2013)
Braik, M., Sheta, A.F., Ayesh, A.: Image Enhancement Using Particle Swarm Optimization. Paper presented at the World congress on engineering (2007)
De Castro, L.N., Timmis, J.: Artificial immune systems: a new computational intelligence approach. Springer (2002)
Gogna, A., Tayal, A.: Comparative analysis of evolutionary algorithms for image enhancement. International Journal of Metaheuristics 2(1), 80–100 (2012)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using MATLAB, vol. 2. Gatesmark Publishing, Knoxville (2009)
Gorai, A., Ghosh, A. (2009). Gray-level Image Enhancement By Particle Swarm Optimization. Paper presented at the World Congress on Nature & Biologically Inspired Computing, NaBIC 2009 (2009)
Hashemi, S., Kiani, S., Noroozi, N., Moghaddam, M.E.: An image contrast enhancement method based on genetic algorithm. Pattern Recognition Letters 31(13), 1816–1824 (2010)
Hassanzadeh, T., Vojodi, H., Mahmoudi, F.: Non-linear grayscale image enhancement based on firefly algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part II. LNCS, vol. 7077, pp. 174–181. Springer, Heidelberg (2011)
Hormozi, E., Akbari, M.K., Javan, M.S.: Performance evaluation of a fraud detection system based artificial immune system on the cloud. Paper presented at the 2013 8th International Conference on Computer Science & Education (ICCSE), April 26-28 (2013)
Ji, Z., Dasgupta, D.: Revisiting negative selection algorithms. Evolutionary Computation 15(2), 223–251 (2007)
Keijzers, S., Maandag, P., Marchiori, E., Sprinkhuizen-Kuyper, I.: Image Similarity Search using a Negative Selection Algorithm. Paper presented at the Advances in Artificial Life, ECAL (2013)
Mahapatra, P.K., Kaur, M., Sethi, S., Thareja, R., Kumar, A., Devi, S.: Improved thresholding based on negative selection algorithm (NSA). Evolutionary Intelligence, 1–14 (2013)
Maini, R., Aggarwal, H.: A comprehensive review of image enhancement techniques. Journal of Computing 2(3), 8–13 (2010)
Munteanu, C., Rosa, A.: Gray-scale image enhancement as an automatic process driven by evolution. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(2), 1292–1298 (2004)
Shyu, M.-S., Leou, J.-J.: A genetic algorithm approach to color image enhancement. Pattern Recognition 31(7), 871–880 (1998)
Thumati, B.T., Halligan, G.R.: A Novel Fault Diagnostics and Prediction Scheme Using a Nonlinear Observer With Artificial Immune System as an Online Approximator. IEEE Transactions on Control Systems Technology 21(3), 569–578 (2013)
Wachowiak, M.P., SmolÃková, R., Zheng, Y., Zurada, J.M., Elmaghraby, A.S.: An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3), 289–301 (2004)
Wang, W., Gao, S., Tang, Z.: Improved pattern recognition with complex artificial immune system. Soft Computing 13(12), 1209–1217 (2009)
Zheng, H., Li, L.: An artificial immune approach for vehicle detection from high resolution space imagery. International Journal of Computer Science and Network Security 7(2), 67–72 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ganguli, S., Mahapatra, P.K., Kumar, A. (2015). Artificial Immune System Based Image Enhancement Technique. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-11218-3_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11217-6
Online ISBN: 978-3-319-11218-3
eBook Packages: EngineeringEngineering (R0)