Decision-based order statistic filters

The authors propose two adaptive order statistic filters which generate outputs based on the results of hypothesis tests. To reduce edge shifting of the median filter, a modified median filter (MMF) is proposed. First, a hypothesis test is applied to detect the edge location. Then, whenever an edge is detected, the MMF generates the output by modifying the median output based on a comparison of median value to the current input value. The MMF can retain more fine details than the median filter with the same window size, as well as reduce the output mean square error which is caused mainly by edge shifting. To suppress nonimpulsive noise as well as impulsive noise, an L-type structure is combined with the MMF. The proposed filter generates the output by using asymmetric alpha -trimming on the MMF output. In addition to noise suppression, it can enhance the gradient of blurred edges provided that the size of the window is greater than the transition width of the blurred edges. Experimental results for one- and two-dimensional signals are presented. >

[1]  Yong Hoon Lee,et al.  An edge gradient enhancing adaptive order statistic filter , 1986, IEEE Trans. Acoust. Speech Signal Process..

[2]  Ioannis Pitas,et al.  Nonlinear mean filters in image processing , 1986, IEEE Trans. Acoust. Speech Signal Process..

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

[4]  P. Vaidyanathan,et al.  Application of two-dimensional generalized mean filtering for removal of impulse noises from images , 1984 .

[5]  William A. Pearlman,et al.  Edge-preserving noise filtering based on adaptive windowing , 1988 .

[6]  Lawrence R. Rabiner,et al.  Applications of a nonlinear smoothing algorithm to speech processing , 1975 .

[7]  Thomas S. Huang,et al.  The Effect of Median Filtering on Edge Estimation and Detection , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  B. Frieden.  A new restoring algorithm for the preferential enhancement of edge gradients , 1976 .

[9]  G. Arce,et al.  State description for the root-signal set of median filters , 1982 .

[10]  Yong Hoon Lee,et al.  Generalized median filtering and related nonlinear filtering techniques , 1985, IEEE Trans. Acoust. Speech Signal Process..

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

[12]  Dan E. Dudgeon,et al.  Multidimensional Digital Signal Processing , 1983 .

[13]  R. Bernstein.  Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images , 1987 .

[14]  Thomas S. Huang,et al.  A generalization of median filtering using linear combinations of order statistics , 1983 .

[15]  C. Pomalaza-ráez,et al.  An adaptative, nonlinear edge-preserving filter , 1984 .

[16]  T. Nodes,et al.  The Output Distribution of Median Type Filters , 1984, IEEE Trans. Commun..

[17]  Josef Schmee,et al.  Outliers in Statistical Data (2nd ed.) , 1986 .

[18]  Thomas S. Huang,et al.  Two-Dimensional Digital Signal Processing I , 1981 .

[19]  D. Rasch,et al.  Sarhan, A. E., and B. G. Greenberg (Ed.): Contributions to Order Statistics. John Wiley & Sons, New York – London 1962; XXV + 482 S., $ 11,25 , 1964 .

[20]  Georg Heygster.  Rank filters in digital image processing , 1982, Comput. Graph. Image Process..

[21]  Yrjö Neuvo,et al.  FIR-median hybrid filters , 1987, IEEE Trans. Acoust. Speech Signal Process..

[22]  Sanjit K. Mitra,et al.  A New Algorithm for Image Edge Extraction Using a Statistical Classifier Approach , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  A. Kundu,et al.  A computationally efficient approach to the removal of impulse noise from digitized speech , 1987, IEEE Trans. Acoust. Speech Signal Process..