Abstract
An approach to image enhancement through artificial neural network’s (ANN) processing is proposed. The structure and weights of ANN are tuned with use of evolutionary concept. Each image is processed in pixel-by-pixel manner using pixels’ local characteristics that are calculated approximately to increase the processing speed but preserving satisfactory calculations’ error. The two-step procedure for image enhancement is proposed: (1) local level processing using ANN; (2) global level autoleveling algorithm. The results for the proposed two-step image enhancement procedure are presented and compared with that of some alternative approaches.
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References
Gonzalez, R., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)
Jahne, B.: Digital Image Processing. Springer, Berlin, Heidelberg, New York (2002)
Woodell, G.A., Jobson, D.J., Rahman, Z., Hines, G.D.: Enhancement of Imagery in Poor Visibility Conditions // Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV. In: Proc. SPIE, vol. 5778 (2005)
Tsoy, Y.R., Spitsyn, V.G.: Using Genetic Algorithm with Adaptive Mutation Mechanism for Neural Networks Design and Training. In: Optical Memory and Neural Networks, vol. 13(4), pp. 225–232. Allerton Press, Inc., New York (2004)
Tsoy, Y.R., Spitsyn, V.G.: Analysis of Genetic Algorithm with Dynamic Population Sizing. In: Proc. of the V Int. Conf. on Artificial Intelligent Systems (AIS 2005), Dyvnomorskoe, Russia (2005)
Papadopoulos, D., Schneider, D., Meier-Eiss, J., Arber, W., Lenski, R.E., Blot, M.: Genomic Evolution during a 10000-generation Experiment with Bacteria. Proc. Natl. Acad. Sci. USA, 96, 3807–3812 (1999)
Schuster, P.: Molecular Insights into Evolution of Phenotypes. In: Crutchfield, J.P., Schuster, P. (eds.) Evolutionary Dynamics – Exploring the Interplay of Accident, Selection, Neutrality, and Function, Oxford Univ. Press, Oxford (2002)
Fukushima, K., Miyake, S.: Neocognitron: A New Algorithm for Pattern Recognition Tolerant of Deformations and Shifts in Position. Pattern Recognition 15(6), 455–469 (1982)
Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proc. Natl. Acad. Sci., USA, 79(8), 2554–2558 (1982)
Kohonen, T.: The Self Organizing Map. Proc. of IEEE 78, 1464–1479 (1990)
Munteanu, C., Rosa, A.: Gray-Scale Image Enhancement as an Automatic Process Driven by Evolution. IEEE Trans. on Systems, Man, and Cybernetics – part B: Cybernetics 34(2) (2004)
Lindley, C.A.: Practical Image Processing in C: Acquisition, Manipulation, Storage. John Wiley & Sons, Inc., New York (1991)
Rahman, Z., Jobson, D.J., Woodell, G.A., Hines, G.D.: Image Enhancement, Image Quality and Noise. In: Proc. of SPIE Photonic Devices and Algorithms for Computing VII (2005)
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Tsoy, Y., Spitsyn, V. (2006). Digital Images Enhancement with Use of Evolving Neural Networks. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_60
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DOI: https://doi.org/10.1007/11844297_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38990-3
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