Abstract
To overcome the problems of losing detail geometric information of images and tending to amplify noise, which exist in traditional image enhancement methods, an adaptive image enhancement method based on Contourlet transform and improved ant colony algorithm is proposed. Firstly, we obtain the coefficients in different scales and different directions by image decomposition using the Contourlet transform. Then, we adopt an adaptive enhancement function with the ability of both feature enhancement and noise reduction to modify the Contourlet coefficients nonlinearly, and use an improved ant colony algorithm to adaptively adjust the parameters of the enhancement function. To find the optimal parameters, a novel evaluation criterion for image enhancement is introduced. Finally, we obtain the enhanced image by Contourlet inverse transform. The experimental results show that our method obtains significant performance in feature enhancement with low contrast and noise reduction over the wavelet-based and Contourlet-based non-adaptive image enhancement methods.
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References
Xie, M., Wang, Z.: Image enhancement based on edge-directed diffusion. Acta Photonica Sinica 34(9), 1420–1424 (2005)
Do, M.N., Vetterli, M.: The Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Transactions on Image Processing 14(12), 2091–2106 (2005)
Eslami, R., Radha, H.: Translation-Invariant Contourlet Transform and Its Application to Image Denoising. IEEE Trans. on Image Processing 15(11), 3362–3374 (2006)
Sha, Y.-H., Liu, F., Jiao, L.-C.: SAR Image Enhancement Based on Nonsubsampled Contourlet Transform. Journal of Electronics & Information Technology 31(7), 1716–1721 (2009)
He, L., Qu, S., Zhang, D.: Image enhancement based on inter-scale correlations of nonsubsampled contourlet coefficients. Journal of Northwestern Polytechnical University 28(1), 42–46 (2010)
Do, M.N., Vetterli, M.: Contourlets: a directional multiresolution image representation. In: International Conference on Image Processing, vol. 1, pp. 357–360 (2002)
Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)
Bamberger, R.H., Smith, M.J.T.: A filter bank for the directional decomposition of images: Theory and design. IEEE Trans. Signal Process. 40(4), 882–893 (1992)
Yuichi, T., Masaaki, I., Nguyen Truong, Q.: Multiresolution image representation using combined 2-D and 1-D directional filter banks. IEEE Transactions on Image Processing 18(2), 269–280 (2009)
Gao, W.: Study on Immunized Ant Colony Optimization, ICNC 2007. In: Third International Conference on Natural Computation, vol. 4, pp. 792–796 (2007)
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Li, L., Guo, Q., Gu, D. (2012). An Adaptive Image Enhancement Method Based on Contourlet Transform and Improved Ant Colony Algorithm. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_56
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DOI: https://doi.org/10.1007/978-3-642-31919-8_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
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