Abstract
Images suffer from poor contrast due to insufficient illumination. The dynamic range of pixel intensities in low-contrast images is very limited, thus not revealing the complete details in the image. Contrast enhancement techniques are used to improve the visual quality of such low-contrast images. Many blind algorithms have been developed to enhance the image without understanding whether it requires such an enhancement procedure. In this paper, we present an automatic contrast enhancement method where the need of the contrast enhancement is ascertained by modelling image contrast factor and using log-likelihood function. Once the need is identified, particle swarm optimization-based stochastic resonance is applied to improve the contrast of the image. The proposed method significantly improves the contrast and simultaneously preserves the brightness of the low-contrast images without introducing any artefacts. Quantitative evaluation of a comparative experimentation with a few conventional enhancement methods demonstrates that the proposed method achieves better-quality results.
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References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)
Ketcham, D.J., Lowe R., Weber W.: Real Time Image Enhancement Techniques. Seminar on Image Processing. Hughes Aircrafts, pp. 1–6 (1996)
Land, E. H.: The Retinex theory for color vision. Scientific American published by W.H. Freeman and company 660 market street, San Francisco, California. vol. 237. No 6, pp. 108–128 (2007)
Jobson, D.J., Rahman, Z., Woodell, G.A.: A multi-scale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Proces. 6(7), 965–976 (1997). doi:10.1109/83.597272
Jain, A.K.: Fundamentals of Digital Image Processing. Englewood Cliffs, Prentice Hall (1989)
Kwok, N.M., Ha, Q.P., Liu, D., Fang, G.: Contrast enhancement and intensity preservation for gray-level images using multi-objective particle swarm optimization. IEEE Trans. Autom. Sci. Eng. 6(1), 145–155 (2009). doi:10.1109/TASE.2008.917053
Cherifi, D., Beghdadi, A., Belbachir, A.H.: Color contrast enhancement method using steerable pyramid transform. Signal Image Video Proces. 4(2), 247–262 (2010). doi:10.1007/s11760-009-0115-6
Benzi, R., Sutera, S., Vulpiani, A.: The mechanism of stochastic resonance. J. Phys. A Math. General 14(11), L453–L457 (1981). doi:10.1088/0305-4470/14/11/006
Miyamoto, R.T., et al.: Stochastic resonance of a threshold detector: image visualization and explanation. Proc. IEEE Int. Symp. Circuit Syst. (ISCAS) 4, IV-521–V-523 (2002). doi:10.1109/ISCAS.2002.1010507
Vaudelle, F., Gazengel, J., Rivoire, G., Godivier, X., Chapeau-Blondeau, F.: Stochastic resonance and noise-enhanced transmission of spatial signals in optics: the case of scattering. J. Optic. Soc. Am. B 15(11), 2674–2680 (1998)
Ye, Q., Huang, H., Zhang, C.: Image enhancement using stochastic resonance. ICIP 1, 263–266 (2004). doi:10.1109/ICIP.2004.1418740
Jha, R.K., Biswas, P.K., Chatterji, B.N.: Enhancement of digital images using stochastic resonance. TENCON, 1–6 (2005). doi:10.1109/TENCON.2005.301203
Chouhan, R., Kumar, C.P., Kumar, R., Jha, R.K.: Contrast enhancement of dark images using stochastic resonance in wavelet domain. Int. J. Mach. Learn. Comput., 2(5) (2012). doi:10.7763/IJMLC.2012.V2.220
Mansoor Roomi, S.M., Karthikeyan, J.P., Shankar, R.: A contrast enhancement based on visual significance. J. Indian Inst. Sci. 79, 89–97 (1999)
Stormont, D.P.: An online Bayesian classifier for object identification. IEEE Int. Workshop Secur. Rescue Robot. Safety, 1–5 (2007). doi:10.1109/SSRR.2007.4381283
Benzi, R., Parisi, G., Sutera, A., Vulpiani, A.: Stochastic resonance in climatic change. Tellus 34, 10–16 (1982)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Sys. Man. Cyber. 9(1), 6266 (1979). doi:10.1109/TSMC.1979.4310076
Kennedy, J., Eberhart, R.: Particle swarm optimization. Proc. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995). doi:10.1109/ICNN.1995.488968
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, ISBN: 978-0-471-05669-0 (2001)
Zuiderveld, K.: Contrast Limited Adaptive Histograph Equalization. Graphic Gems IV. Academic Press Professional, San Diego (1994)
Braik, M., Sheta, A., Ayesh, A.: Image enhancement using particle swarm optimization. In: Proceedings of the world congress on engineering (WCE), vol.I, ISBN:978-988-98671-5-7 (2007)
Maragatham, G., Mansoor Roomi, S.M.: An automatic contrast enhancement method based on stochastic resonance. ICCCNT. 1–7 (2013). doi:10.1109/ICCCNT.2013.6726602
Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consum. Electron. 51(4), 1326–1334 (2005)
Maragatham, G., Mansoor Roomi, S. M, Manoj Prabu, T.: Contrast enhancement by object based histogram equalization. WICT, 1118–1122 (2011)
Jha, R.K., Chouhan, R.: Noise-induced contrast enhancement using stochastic resonance on singular values. SIVP 8, 339–347 (2012)
Hashemi, S., Kiani, S., Noroozi, N., Ebrahimi, M.M.: An image contrast enhancement method based on genetic algorithm. Pattern Recognit. Lett. 31, 18161824 (2010)
Draa, A., Bouaziz, A.: An Artificial Bee Colony Algorithm for Image Contrast Enhancement, vol. 16. Elsevier, Amsterdam (2014). doi:10.1016/j.swevo.2014.01.003
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Maragatham, G., Roomi, S.M.M. PSO-based stochastic resonance for automatic contrast enhancement of images. SIViP 10, 207–214 (2016). https://doi.org/10.1007/s11760-014-0728-2
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DOI: https://doi.org/10.1007/s11760-014-0728-2