Abstract
In this paper, we present a block overlapped intensity-pair distribution based image enhancement algorithm. Instead of using the intensity-pair distribution of the whole image, this proposed algorithm takes the intensity-pair distribution block-wise and maps the intensity of the center pixel according to an expansion function. Analyzing the intensity difference of the intensity-pair, two different expansion force sets are generated for contrast stretch: one for soft edges, another for strong edges. In addition, a set of anti-expansion force is generated for smooth regions to avoid noticeable change. The contrast stretch and over-enhancement are controlled with a linear magnitude mapping function instead of a non-linear one. This linear mapping preserves the relative contrast enhancement ratio between the gray levels. The local information from blocks easily facilitates the contrast enhancement, brings out subtle edge information, and removes noises from the image.
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Jen, T., Hsieh, B., Wang, S.: Image contrast enhancement based on intensity-pair distribution. In: Proc. Int. Conf. Image Processing, vol. 1, pp. 913–916 (2005)
Starck, J., Murtagh, F., Candes, E.J., Donnoho, D.L.: Gray and color image contrast enhancement by the curvelet transform. IEEE Trans. Image Processing 12(6), 706–717 (2003)
Velde, K.V.: Multi-scale color image enhancement. In: Proc. Int. Conf. Image Processing, vol. 3, pp. 584–587 (1999)
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 Processing 13, 416–429 (2004)
Chin, W.A., S.H., Tan, E.C.: Novel approach to automated fingerprint recognition. In: Proc. IEE Vision, Image and Signal Processing, vol. 145, pp. 160–166 (1998)
Torre, A., Peinado, A.M., Segura, J.C., Perez-Cordoba, J.L., Benitez, M.C., Rubio, A.J.: Histogram equalization of speech representation for robust speech recognition. IEEE Trans. Speech Audio Processing 13, 355–366 (2005)
Pizer, S.M.: The medical image display and analysis group at the university of north carolina: Reminiscences and philosophy. IEEE Trans. Medical Imaging 22, 2–10 (2003)
Chen, S.–D., Ramli, A.R.: Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation. IEEE Trans. Consumer Electronics 49(4), 1301–1309 (2003)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley, Reading (1992)
Kim, Y.-K.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consumer Electronics 43(1), 1–8 (1997)
Kim, Y.K., Paik, J.K., Kang, B.S.: Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. on Consumer Electronics 44(1), 82–86 (1998)
Kim, J.-Y., Kim, L.-S., Hwang, S.-H.: An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans. Circuits and Systems for Video Technology 11, 475–484 (2001)
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Kabir, M.H., Abdullah-Al-Wadud, M., Chae, O. (2006). Image Contrast Enhancement Based on Block-Wise Intensity-Pair Distribution with Two Expansion Forces. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_25
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DOI: https://doi.org/10.1007/11892755_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46556-0
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