Fuzzy Filters for Noise Reduction in Images

In this chapter, seven fuzzy filters for noise reduction in images are introduced. These seven fuzzy filters include the Gaussian fuzzy filter with median center (GMED), the symmetrical triangular fuzzy filter with median center (TMED), the asymmetrical triangular fuzzy filter with median center (ATMED), the Gaussian fuzzy filter with moving average center (GMAV), the symmetrical triangular fuzzy filter with moving average center (TMAV), the asymmetrical triangular fuzzy filter with moving average center (ATMAV), and the decreasing weight fuzzy filter with moving average center (DWMAV). Each of these fuzzy filters, applies a weighted membership function to an image within a window to determine the center pixel, is easy and fast to implement. Simulation results on the filtering performance of these seven fuzzy filters and the standard median filter (MED) and moving average filter (MAV) on images contaminated with low, medium, high impulse and random noises are presented. Results indicate that these seven fuzzy filters achieve varying successes in noise reduction in images as compared to the standard MED and MAV filters.

[1]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[2]  Ioannis Pitas,et al.  Nonlinear Digital Filters - Principles and Applications , 1990, The Springer International Series in Engineering and Computer Science.

[3]  Hon Keung Kwan,et al.  Median filtering using fuzzy concept , 1993, Proceedings of 36th Midwest Symposium on Circuits and Systems.

[4]  Yutaka Murata,et al.  Fuzzy filters for image smoothing , 1994, Electronic Imaging.

[5]  F. Russo,et al.  Data-dependent filtering using the fuzzy inference , 1995, Proceedings of 1995 IEEE Instrumentation and Measurement Technology Conference - IMTC '95.

[6]  J. Astola,et al.  Binary polynomial transforms and nonlinear digital filters , 1995 .

[7]  Kaoru Arakawa,et al.  Median filter based on fuzzy rules and its application to image restoration , 1996, Fuzzy Sets Syst..

[8]  F. Russo,et al.  A fuzzy filter for images corrupted by impulse noise , 1996, IEEE Signal Processing Letters.

[9]  Giovanni Ramponi,et al.  Removal of impulse noise using a FIRE filter , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[10]  Pao-Ta Yu,et al.  Weighted fuzzy mean filters for image processing , 1997, Fuzzy Sets Syst..

[11]  F. Russo Noise cancellation using nonlinear fuzzy filters , 1997, IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings.

[12]  Fabrizio Russo Recent advances in fuzzy techniques for image enhancement , 1998, IEEE Trans. Instrum. Meas..

[13]  Fabrizio Russo,et al.  FIRE operators for image processing , 1999, Fuzzy Sets Syst..

[14]  Dimitri Van De Ville,et al.  New fuzzy filter for Gaussian noise reduction , 2000, IS&T/SPIE Electronic Imaging.

[15]  Yau-Hwang Kuo,et al.  Adaptive Fuzzy Filter and Its Application to Image Enhancement , 2000 .

[16]  Sanjit K. Mitra,et al.  Nonlinear image processing , 2000 .

[17]  Mohammad Bagher Menhaj,et al.  A Fuzzy Logic Control Based Approach for Image Filtering , 2000 .

[18]  Kaoru Arakawa,et al.  Fuzzy Rule-Based Image Processing with Optimization , 2000 .

[19]  Etienne Kerre,et al.  Fuzzy techniques in image processing , 2000 .

[20]  Dimitri Van De Ville,et al.  A comparative study of classical and fuzzy filters for noise reduction , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[21]  D. Van De Ville,et al.  An overview of classical and fuzzy-classical filters for noise reduction , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[22]  Dimitri Van De Ville,et al.  An overview of fuzzy filters for noise reduction , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[23]  Hon Keung Kwan,et al.  Fuzzy filters for image filtering , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..