Abstract
Contrast enhancement is a fundamental procedure in applications requiring image processing. Indeed, image enhancement contributes critically to the success of subsequent operations such as feature detection, pattern recognition and other higher-level processing tasks. Of interest among methods available for contrast enhancement is the intensity modification approach, which is based on the statistics of pixels in a given image. However, due to variations in the imaging condition and the nature of the scene being captured, it turns out that global manipulation of an image may be vulnerable to a noticeable quality degradation from distortion and noise. This chapter is devoted to the development of a local intensity equalization strategy together with mechanisms to remedy artifacts produced by the enhancement while ensuring a better image for viewing. To this end, the original image is subdivided randomly into sectors, which are equalized independently. A Gaussian weighting factor is further used to remove discontinuities along sector boundaries. To achieve simultaneously the multiple objectives of contrast enhancement and viewing distortion reduction, a suitable optimization algorithm is required to determine sector locations and the associated weighting factor. For this, a particle-swarm optimization algorithm is adopted in the proposed image enhancement method. This algorithm helps optimize the Gaussian weighting parameters for discontinuity removal and determine the local region where enhancement is applied. Following comprehensive descriptions on the methodology, this chapter presents some real-life images for illustration and verification of the effectiveness of the proposed approach.
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References
Chen, Q., Xu, X., Sun, Q., Xia, D.: A solution to the deficiencies of image enhancement. Signal Process. 90, 44–56 (2010)
Chen, S.D., Ramli, A.R.: Minimum mean brightness error bi-histogram equalization in contrast enhancement. IEEE Trans. Consumer Electron. 49(4), 1310–1319 (2003)
Chen, S.Y., Li, Y.F., Zhang, J.W.: Vision processing for realtime 3d data acquisition based on coded structured light. IEEE Trans. Image Process. 17(2), 167–176 (2008)
Cheng, H.D., Shi, X.J.: A simple and effective histogram equalization approach to image enhancement. Digit. Signal Process. 14, 158–170 (2004)
Clerc, M., Kennedy, J.: The particle-swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Frosio, J., Ferrigno, G., Borghese, N.A.: Enhancing digital cephalic radiography with mixture models and local gamma correction. IEEE Trans. Med. Imaging 25(1), 113–121 (2006)
Hanmandlu, M., Jha, D., Sharma, R.: Color image enhancement by fuzzy intensification. Pattern Recognit. Lett. 24, 81–87 (2003)
Kao, W.C., Chen, Y.J.: Multistage bilateral noise-filtering and edge detection for color image enhancement. IEEE Trans. Consumer Electron. 51(4), 1346–1351 (2005)
Kim, J.Y., Kim, L.S., Hwang, S.H.: An advanced contrast enhancement using partially overlapped subblock histogram equalization. IEEE Trans. Circuits Syst. Video Technol. 11(4), 475–484 (2001)
Kim, T.K., Park, J.K., Kang, B.S.: Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consumer Electron. 44(1), 82–87 (1998)
Kong, N.S.P., Ibrahim, H.: Color image enhancement using brightness preserving dynamic histogram equalization. IEEE Trans. Consumer Electron. 54(4), 1962–1967 (2008)
Kwok, N.M., Ha, Q.P., Liu, D.K., Fang, G.: Contrast enhancement and intensity preservation for gray-level images using multiobjective particle-swarm optimization. IEEE Trans. Autom. Sci. Eng. 6(1), 145–155 (2009)
Lee, S., Chang, L.M., Skibniewski, M.: Automated recognition of surface defects using digital color image processing. Autom. Constr. 15, 540–549 (2006)
Li, F., Jia, X., Fraser, D.: Superresolution reconstruction of multispectral data for improved classification. IEEE Geosci. Remote Sens. Lett. 6(4), 689–693 (2009)
Naik, S.K., Murhty, C.A.: Hue-preserving color image enhancement without gamut problem. IEEE Trans. Image Process. 12(12), 1591–1598 (2003)
Pei, S.C., Zeng, Y.C., Chang, C.H.: Virtual restoration of ancient chinese paintings using color contrast enhancement and lacuna texture synthesis. IEEE Trans. Image Process. 13(3), 416–429 (2004)
Schavemaker, J.G.M., Reinders, M.J.T., Gerbrands, J.J., Backer, E.: Image sharpening by morphological filtering. Pattern Recognit. 33, 997–1012 (2000)
Seow, M.J., Asari, V.K.: Ratio rule and homomorphic filter for enhancement of digital colour image. Neurocomputing 69, 954–958 (2006)
Shyu, M.S., Leou, J.J.: A genetic algorithm approach to color image enhancement. Pattern Recognit. 31(7), 871–880 (1998)
Starck, J.L., Murtagh, F., Candès, E.J., Donoho, D.L.: Gray and color image contrast enhancement by the curvelet transform. IEEE Trans. Image Process. 12(6), 706–717 (2003)
Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000)
Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Trans. Consumer Electron. 51(4), 1326–1334 (2005)
Wang, D., Kwok, N.M., Liu, D.K., Ha, Q.P.: Ranked Pareto particle-swarm optimization for mobile robot motion planning. Design and Control of Intelligent Robotic Systems, pp. 97–118. Springer-Verlag, Berlin, Heidelberg (2009)
Wang, Y., Chen, Q., Zhang, B.: Image enhancement based on equal area dualistic subimage histogram equalization method. IEEE Trans. Consumer Electron. 45(1), 68–75 (1999)
Zhu, H., Chan, H.Y., Lam, F.K.: Image contrast enhancement by constrained local histogram equalization. Comput. Vision Image Underst. 73(2), 281–290 (1999)
Zuiderveld, K.: Contrast limited adaptive histogram equalization, pp. 474–485. Academic Press Professional, Inc., San Diego, CA, USA (1994). http://portal.acm.org/citation.cfm?id=180895.180940
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Kwok, N.M., Wang, D., Ha, Q.P., Fang, G., Chen, S.Y. (2013). Locally-Equalized Image Contrast Enhancement Using PSO-Tuned Sectorized Equalization. In: Chatterjee, A., Siarry, P. (eds) Computational Intelligence in Image Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30621-1_2
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DOI: https://doi.org/10.1007/978-3-642-30621-1_2
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