Abstract
A new algorithm for the contrast enhancement of images, based on the theory of Partitioned Iterated Function System (PIFS), is presented. A PIFS consists of contractive transformations, such that the original image is the fixed point of the union of these transformations. Each transformation involves the contractive affine spatial transform of a square block, as well as the linear transform of the gray levels of its pixels. The PIFS is used in order to create a lowpass version of the original image. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against two other widely used contrast enhancement methods.
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References
Lim, J.S.: Two-dimensional Signal and Image Processing. Prentice Hall, New Jersey (1990)
Umbaugh, S.E.: Computer Vision and Image Processing: A Practical Approach Using CVIPTools, 1st edn. Prentice-Hall, Englewood Cliffs (1997)
Sund, T., Møystad, A.: Sliding window adaptive histogram equalization of intraoral radiographs: effect on image quality. Dentomaxillofacial Radiology 35, 133–138 (2006)
Ramponi, P.G., Mathews, V.J.: Adaptive unsharp masking for contrast enhancement. In: International Conference on Image Processing, vol. 1, p. 267 (1997)
Badamchizadeh, M.A., Aghagolzadeh, A.: Comparative study of unsharp masking methods for image enhancement. In: Image and Graphics Proceedings, pp. 27–30 (2004)
Arici, T., Altunbasak, Y.: Image local contrast enhancement using adaptive non linear filters. In: IEEE international conference on Image Processing (to be published)
Barnsley, M.F., Hurd, L.P.: Fractal Image Compression. AK Press, Massachusetts (1993)
Jacquin, E.: Fractal image coding: a review. Proceedings of the IEEE 81(10), 1451–1465 (1993)
Thomas, L., Deravi, F.: Region-based fractal image compression using heuristic search. IEEE Trans. on Image Processing 4(6), 832–838 (1995)
Nikiel, S.: Integration of iterated function systems and vector graphics for aesthetics. Computers & Graphics 30, 277–283 (2006)
Fan, K.C., Chang, J.C., Kan, K.S.: Improvement of image-compression quality via block classification and coefficient diffusion. In: Proc. SPIE, vol. 2501, pp. 1727–1736 (1995)
Kuan, J.K.P., Lewis, P.H.: Fast k nearest neighbour search for R-tree family. In: Proceedings on First International Conf. on Information, Communications, and Signal Processing. Singapore, pp. 924–928 (1997)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Commun. ACM 18(9), 509–517 (1975)
Jacquin: Image coding based on a fractal theory of iterated contractive image transformations. IEEE Trans. Image Proc. 1, 18–30 (1992)
Chen, S.K., Hollender, L.: Linear unsharp mask filtering of linear cross-sectional tomograms of the posterior mandible. Swed. Dent. J. 19(4), 139–147 (1995)
Ramponi, G.: A cubic unsharp masking technique for contrast enhancement. Signal Processing 67(2), 211–222 (1998)
De Vries, F.P.: Automatic adaptive brightness independent contrast enhancement. Signal Process 21, 169–182 (1990)
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Economopoulos, T., Asvestas, P., Matsopoulos, G. (2007). Contrast Enhancement of Images Using Partitioned Iterated Function Systems. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_45
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DOI: https://doi.org/10.1007/978-3-540-74607-2_45
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