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Adaptive image enhancement algorithm combining kernel regression and local homogeneity. (English) Zbl 1204.94026

Summary: It is known that many image enhancement methods have a tradeoff between noise suppression and edge enhancement. In this paper, we propose a new technique for image enhancement filtering and explain it in human visual perception theory. It combines kernel regression and local homogeneity and evaluates the restoration performance of smoothing method. First, image is filtered in kernel regression. Then image local homogeneity computation is introduced which offers adaptive selection about further smoothing. The overall effect of this algorithm is effective about noise reduction and edge enhancement. Experiment results show that this algorithm has better performance in image edge enhancement, contrast enhancement, and noise suppression.

MSC:

94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
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References:

[1] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, 1990. · Zbl 05111848 · doi:10.1109/34.56205
[2] B. M. Romeny, Geometry-Driven Diffusion in Computer Vision, 1994. · Zbl 0832.68111
[3] J. Weickert, Anisotropic Diffusion in Image Processing, European Consortium for Mathematics in Industry, B. G. Teubner, Stuttgart, Germany, 1998. · Zbl 0886.68131
[4] L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D, vol. 60, no. 1-4, pp. 259-268, 1992. · Zbl 0780.49028 · doi:10.1016/0167-2789(92)90242-F
[5] T. F. Chan, J. Shen, and L. Vese, “Variational PDE models in image processing,” Notices of the American Mathematical Society, vol. 50, no. 1, pp. 14-26, 2003. · Zbl 1168.94315
[6] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of the IEEE 6th International Conference on Computer Vision, pp. 839-846, Bombay, India, January 1998.
[7] M. Elad, “Analysis of the bilateral filter,” in Proceedings of the 36th Asilomar Conference on Signals Systems and Computers, pp. 483-487, Pacific Grove, Calif, USA, November 2002.
[8] M. Elad, “On the origin of the bilateral filter and ways to improve it,” IEEE Transactions on Image Processing, vol. 11, no. 10, pp. 1141-1151, 2002. · Zbl 05452902 · doi:10.1109/TIP.2002.801126
[9] T. R. Jones, F. Durand, and M. Desbrun, “Non-iterative, feature-preserving mesh smoothing,” in Proceedings of the Conference on Sketches & Applications: in Conjunction with the 30th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’03), pp. 943-949, San Diego, Calif, USA, July 2003. · doi:10.1145/1201775.882367
[10] S. Fleishman, I. Drori, and D. Cohen-Or, “Bilateral mesh denoising,” in Proceedings of the Conference on Sketches & Applications: in Conjunction with the 30th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’03), pp. 950-953, San Diego, Calif, USA, July 2003. · doi:10.1145/1201775.882368
[11] D. Barash, “Bilateral filtering and anisotropic diffusion: towards a unified viewpoint,” in Proceedings of the 3rd International Conference Scale-Space and Morphology, pp. 273-280, 2001. · Zbl 0991.68589
[12] R. Xu and S. N. Pattanaik, “A novel Monte Carlo noise reduction operator,” IEEE Computer Graphics and Applications, vol. 25, no. 2, pp. 31-35, 2005. · Zbl 05085627 · doi:10.1109/MCG.2005.31
[13] K. Fukunaga and L. D. Hostetler, “The estimation of the gradient of a density function, with applications in pattern recognition,” IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32-40, 1975. · Zbl 0297.62025 · doi:10.1109/TIT.1975.1055330
[14] Y. Cheng, “Mean shift, mode seeking, and clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 8, pp. 790-799, 1995. · Zbl 05112121 · doi:10.1109/34.400568
[15] D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002. · doi:10.1109/34.1000236
[16] J.-S. Zhang, X.-F. Huang, and C.-H. Zhou, “An improved kernel regression method based on Taylor expansion,” Applied Mathematics and Computation, vol. 193, no. 2, pp. 419-429, 2007. · Zbl 1193.94025 · doi:10.1016/j.amc.2007.03.085
[17] E. A. Nadaraya, “On estimating regression,” Theory of Probability and Its Applications, vol. 9, no. 1, pp. 157-159, 1964. · Zbl 0136.40902
[18] G. S. Watson, “Smooth regression analysis,” Sankhy\Ba Series A, vol. 26, pp. 359-372, 1964. · Zbl 0137.13002
[19] S. W. Kuffler, “Discharge patterns and functional organization of mammalian retina,” Journal of Neurophysiology, vol. 16, no. 1, pp. 37-68, 1953.
[20] R. W. Rodieck, “Quantitative analysis of cat retinal ganglion cell response to visual stimuli,” Vision Research, vol. 5, no. 12, pp. 583-601, 1965. · doi:10.1016/0042-6989(65)90033-7
[21] C. Y. Li, X. Pei, Y. Zhow, and H. C. Von Mitzlaff, “Role of the extensive area outside the X-cell receptive field in brightness information transmission,” Vision Research, vol. 31, no. 9, pp. 1529-1540, 1991. · doi:10.1016/0042-6989(91)90130-W
[22] F. Jing, M. Li, H. J. Zhang, and B. Zhang, “Unsupervised image segmentation using local homogeneity analysis,” in Proceedings of the IEEE International Symposium on Circuits and Systems, pp. II456-II459, May 2003.
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