An Efficient Noise Removal Edge Detection Algorithm Based on Wavelet Transform
Subject Areas : Image Processing
1 - Sirjan University of Technology
Keywords: Wavelet Transform, Edge Detection, Gaussian Filter, Multiscale Analysis, Noise Removal, Gaussian Bases, Wavelet Function Derivation, Admissibility Condition, Edge Criteria, N-connected Neighborhoo,
Abstract :
In this paper, we propose an efficient noise robust edge detection technique based on odd Gaussian derivations in the wavelet transform domain. At first, new basis wavelet functions are introduced and the proposed algorithm is explained. The algorithm consists of two stage. The first idea comes from the response multiplication across the derivation and the second one is pruning algorithm which improves fake edges. Our method is applied to the binary and the natural grayscale image in the noise-free and the noisy condition with the different power density. The results are compared with the traditional wavelet edge detection method in the visual and the statistical data in the relevant tables. With the proper selection of the wavelet basis function, an admissible edge response to the significant inhibited noise without the smoothing technique is obtained, and some of the edge detection criteria are improved. The experimental visual and statistical results of studying images show that our method is feasibly strong and has good edge detection performances, in particular, in the high noise contaminated condition. Moreover, to have a better result and improve edge detection criteria, a pruning algorithm as a post processing stage is introduced and applied to the binary and grayscale images. The obtained results, verify that the proposed scheme can detect reasonable edge features and dilute the noise effect properly.
[1] F. Guo, Y. Yang, B. Chen, and L. Guo, "A novel multi-scale edge detection technique based on wavelet analysis with application in multiphase flows," Powder Technology, vol. 202, no. 1-3, pp. 171–177, Aug. 2010.#
[2] C. Lopez-Molina, B. De Baets, H. Bustince, J. Sanz, and E. Barrenechea, "Multiscale edge detection based on Gaussian smoothing and edge tracking," Knowledge-Based Systems, vol. 44, pp. 101–111, May 2013.#
[3] J. Canny, "A computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.#
[4] W. McIlhagga, "The canny edge detector revisited," International Journal of Computer Vision, vol. 91, no. 3, pp. 251–261, Oct. 2010.#
[5] L. Ding and A. Goshtasby, "On the canny edge detector," Pattern Recognition, vol. 34, no. 3, pp. 721–725, Mar. 2001.#
[6] R. C. Gonzalez, R. E. Woods, D. J. Czitrom, and S. Armitage, Digital image processing, 3rd ed. United States: Prentice Hall, 2007.#
[7] S. Mallat and S. Zhong, "Characterization of signals from multiscale edges," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 7, pp. 710–732, Jul. 1992.#
[8] B. M. Sadler and A. Swami, "Analysis of multiscale products for step detection and estimation," IEEE Transactions on Information Theory, vol. 45, no. 3, pp. 1043–1051, Apr. 1999. [9] L. Zhang and P. Bao, "Edge detection by scale multiplication in wavelet domain," Pattern Recognition Letters, vol. 23, no. 14, pp. 1771–1784, Dec. 2002.
[10] Z. Zhu, H. Lu, and Y. Zhao, "Scale multiplication in odd Gabor transform domain for edge detection," Journal of Visual Communication and Image Representation, vol. 18, no. 1, pp. 68–80, Feb. 2007.#
[11] M. Hasanzadeh Mofrad, S. Sadeghi, A. Rezvanian, and M. R. Meybodi, "Cellular edge detection: Combining cellular automata and cellular learning automata," AEU - International Journal of Electronics and Communications, vol. 69, no. 9, pp. 1282–1290, Sep. 2015.#
[12] S. Uguz, U. Sahin, and F. Sahin, "Edge detection with fuzzy cellular automata transition function optimized by PSO," Computers & Electrical Engineering, vol. 43, pp. 180–192, Apr. 2015.#
[13] S. Amrogowicz and Y. Zhao, "An edge detection method using outer totalistic cellular Automata," Neurocomputing, Jun. 2016.#
[14] J. Gu, Y. Pan, and H. Wang, "Research on the improvement of image edge detection algorithm based on artificial neural network," Optik - International Journal for Light and Electron Optics, vol. 126, no. 21, pp. 2974–2978, Nov. 2015.#
[15] C. I. Gonzalez, P. Melin, J. R. Castro, O. Castillo, and O. Mendoza, "Optimization of interval type-2 fuzzy systems for image edge detection," Applied Soft Computing, vol. 47, pp. 631–643, Oct. 2016. #
[16] X. Liu and S. Fang, "A convenient and robust edge detection method based on ant colony optimization," Optics Communications, vol. 353, pp. 147–157, Oct. 2015.#
[17] G. Ma, C. Liu, and D. Huang, "The removal of additional edges in the edge detection of potential field data," Journal of Applied Geophysics, vol. 114, pp. 168–173, Mar. 2015.#
[18] D. Rivero-Castillo, H. Pijeira, and P. Assunçao, "Edge detection based on Krawtchouk polynomials," Journal of Computational and Applied Mathematics, vol. 284, pp. 244–250, Aug. 2015.#
[19] N. Decoster, S. G. Roux, and A. Arnéodo, "A wavelet-based method for multifractal image analysis. II. Applications to synthetic multifractal rough surfaces," The European Physical Journal B, vol. 15, no. 4, pp. 739–764, Jun. 2000.#
[20] Q. Sun, Y. Hou, and Q. Tan, "A subpixel edge detection method based on an arctangent edge model," Optik - International Journal for Light and Electron Optics, vol. 127, no. 14, pp. 5702–5710, Jul. 2016.#
[21] G. J. Tu and H. Karstoft, "Logarithmic dyadic wavelet transform with its applications in edge detection and reconstruction," Applied Soft Computing, vol. 26, pp. 193–201, Jan. 2015.#
[22] B. Zuo and X. Hu, "Edge detection of gravity field using eigenvalue analysis of gravity gradient tensor," Journal of Applied Geophysics, vol. 114, pp. 263–270, Mar. 2015.#
[23] J. Wang, X. Meng, and F. Li, "Improved curvature gravity gradient tensor with principal component analysis and its application in edge detection of gravity data," Journal of Applied Geophysics, vol. 118, pp. 106–114, Jul. 2015.#
[24] M.-Y. Shih and D.-C. Tseng, "A wavelet-based multiresolution edge detection and tracking," Image and Vision Computing, vol. 23, no. 4, pp. 441–451, Apr. 2005.#
[25] PRATT, W.K.: 'DIGITAL IMAGE PROCESSING', (JOHN WILEY & SONS, NEW YORK, USA, 2001. EASED).#
[26] M. K. Geetha and S. Palanivel, "Video classification and shot detection for video retrieval applications," International Journal of Computational Intelligence Systems, vol. 2, no. 1, pp. 39–50, 2009.#
