Comparative Analysis of Noise Filtering Performance of Quadratic Image Filters Article Sidebar

Year 2019, Volume: 2 Issue: 2, 13 - 16, 28.12.2019

Süleyman Uzun Devrim Akgün

Abstract

Quadratic image filters belongto a subclass of nonlinear model known as Volterra filters. Because of the nonlinear characteristics of images, nonlinear image filters generally produce better results than linear filters. In the present study, performance of the Quadratic image filters for Gaussian noise is examined by comparing with Gaussian filter and Median filter. For this purpose, the mask weights used were determined by using Differential Evolution algorithm on synthetic training images. Noise added colourtest images were filtered using Quadratic image filter using the calculated weights and the results were compared with Gaussian filter and Median image filte

Keywords

Differential Evolution Algorithm, Gaussian Filtering, Noise Removal, Quadratic Image Filter

References

• [1]J. C. Russ, The image processing handbook. CRC press, 2016.
• [2]I. Pitas and A. N. Venetsanopoulos, Nonlinear digital filters: principles and applications, vol. 84. Springer Science & Business Media, 2013.
• [3]G. F. Ramponi, G. L. Sicuranza, and W. Ukovich, “A computational method for the design of 2-D nonlinear Volterra filters,” IEEE Trans. Circuits Syst., vol. 35, no. 9, pp. 1095–1102, 1988
• 4] J. Zhang and Y. Pang, "Pipelined robust M-estimate adaptive second-order Volterra filter against impulsive noise." Digital Signal Process., vol. 26, pp. 71–80, Mar. 2014.
• [5] L. Thomas, G. Krishnan, R. A. Mol, and A. Roy, "Removal of Impulsive Noise from MRI Images using Quadratic Filter." Int. J. Eng. Res. Technol., vol. 3, no. 4, pp. 2220–2223, 2014.
• [6] M. B. Meenavathi and K. Rajesh, "Volterra Filtering Techniques for Removal of Gaussian and Mixed Gaussian-Impulse Noise." Int. J. Electr. Robot., vol. 1, no. 2, pp. 1–7, 2007.
• [7] V. S. Hari, V. P. Jagathy Raj, and R. Gopikakumari, "Quadratic filter for the enhancement of edges in retinal images for the efficient detection and localization of diabetic retinopathy." Pattern Anal. Appl., vol. 20, no. 1, pp. 145–165, Feb. 2017.
• [8] A. Chakrabarty, H. Jain, and A. Chatterjee, "Volterra kernel based face recognition using artificial bee colony optimization." Eng. Appl. Artif. Intell., vol. 26, no. 3, pp. 1107–1114, 2013.
• [9] G. Feng, H. Li, J. Dong, and J. Zhang, "Direct Discriminant Analysis Using Volterra Kernels for Face Recognition," 2016, pp. 404–412.
• [10] G. Feng, H. Li, J. Dong, and J. Zhang, "Face recognition based on Volterra kernels direct discriminant analysis and effective feature classification." Inf. Sci. (Ny)., vol. 441, pp. 187–197, 2018.
• [11] V. Bhateja, M. Misra, and S. Urooj, "Non-linear polynomial filters for edge enhancement of mammogram lesions." Comput. Methods Programs Biomed., vol. 129, pp. 125–134, 2016.
• [12] A. Pandey, A. Yadav, and V. Bhateja, "Design of new Volterra filter for mammogram enhancement." in Advances in Intelligent Systems and Computing, vol. 199 AISC, pp. 143–151, 2013.
• [13] Y. Zhou, K. Panetta, and S. Agaian, "Mammogram enhancement using alpha weighted quadratic filter." in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2009, pp. 3681–3684.
• [14] S. Uzun and D. Akgün, "An Accelerated Method for Determining the Weights of Quadratic Image Filters." IEEE Access, vol. 6, 2018.
• [15] S. Uzun and D. Akgün, "Accelerated method for the optimization of quadratic image filter." J. Electron. Imaging, vol. 28, no. 03, p. 1, Jun. 2019.
• [16] V. P. Plagianakos, D. K. Tasoulis, and M. N. Vrahatis, "A review of major application areas of differential evolution." in Advances in differential evolution, Springer, 2008, pp. 197–238.
• [17] W. Eaton John, B. David, and W. Rik, "GNU Octave version 5.1. 0 manual: a high-level interactive language for numerical computations, 2019." URL https//www. gnu. org/software/octave/doc/v5, vol. 1.