A new method for impulsive noise suppression from highly distorted images by using Anfis

Abstract A new impulsive noise elimination filter, entitled Anfis-based impulsive noise removing filter (AIF), which shows a high performance at the restoration of images distorted by impulsive noise, is proposed in this paper. AIF uses Anderson-Darling test values in order to determine the pixels which exhibit impulsive behavior within the image. Extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.

[1]  T. Furuike,et al.  Finite-element structural analysis , 1977 .

[2]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[3]  Vincenzo Piuri,et al.  Digital Median Filters , 2002, J. VLSI Signal Process..

[4]  M. Stephens.  EDF Statistics for Goodness of Fit and Some Comparisons , 1974 .

[5]  T. Nodes,et al.  Median filters: Some modifications and their properties , 1982 .

[6]  John C. Davis,et al.  Contouring: A Guide to the Analysis and Display of Spatial Data , 1992 .

[7]  Murray R. Spiegel,et al.  Schaum's Outline of Theory and Problems of Probability and Statistics , 1980 .

[8]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[9]  M. Emin Yüksel,et al.  Efficient Removal of Impulse Noise from Highly Corrupted Digital Images by a Simple Neuro-Fuzzy Operator , 2003 .

[10]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[11]  A. Willson,et al.  Median filters with adaptive length , 1988 .

[12]  Yrjö Neuvo,et al.  Detail-preserving median based filters in image processing , 1994, Pattern Recognit. Lett..

[13]  Francesco Masulli,et al.  On the structure of a neuro-fuzzy system to forecast chaotic time series , 1996, 1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report.

[14]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[15]  H. Wu,et al.  Adaptive impulse detection using center-weighted median filters , 2001, IEEE Signal Processing Letters.

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

[17]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[18]  Tao Chen,et al.  A new class of median based impulse rejecting filters , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[19]  Michael L. Lightstone,et al.  A new efficient approach for the removal of impulse noise from highly corrupted images , 1996, IEEE Trans. Image Process..

[20]  Jung-Hua Wang,et al.  Histogram-based fuzzy filter for image restoration , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[21]  Takao Hinamoto,et al.  A new adaptive center weighted median filter using counter propagation networks , 2000, J. Frankl. Inst..

[22]  Abdelhak M. Zoubir,et al.  Testing for Impulsive Behavior: A Bootstrap Approach , 2001, Digit. Signal Process..

[23]  Michael G. Strintzis,et al.  A context based adaptive arithmetic coding technique for lossless image compression , 1999, IEEE Signal Processing Letters.

[24]  John W. Tukey,et al.  Nonlinear (nonsuperposable) methods for smoothing data , 1974 .

[25]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..