A class of adaptive directional image smoothing filters

The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates a line segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters.

[1]  John B. Thomas,et al.  Detectors for discrete-time signals in non-Gaussian noise , 1972, IEEE Trans. Inf. Theory.

[2]  Edward J. Coyle,et al.  Adaptive stack filtering under the mean absolute error criterion , 1990, Other Conferences.

[3]  Sanjit K. Mitra,et al.  Image Representation Using Block Pattern Models and Its Image Processing Applications , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  M.R. Azimi-Sadjadi,et al.  Neural network decision directed edge-adaptive Kalman filter , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[5]  L. Silverman,et al.  Image model representation and line-by-line recursive restoration , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

[6]  Philippe Salembier,et al.  Adaptive rank order based filters , 1992, Signal Process..

[7]  Jaakko Astola,et al.  Adaptive Stack Filtering with Application to Image Processin , 1993, IEEE Trans. Signal Process..

[8]  J. Woods,et al.  Kalman filtering in two dimensions: Further results , 1981 .

[9]  Gonzalo R. Arce,et al.  Deterministic properties of the recursive separable median filter , 1987, IEEE Trans. Acoust. Speech Signal Process..

[10]  Gonzalo R. Arce,et al.  Theoretical analysis of the max/Median filter , 1987, IEEE Trans. Acoust. Speech Signal Process..

[11]  G. Eichmann,et al.  Vector median filters , 1987 .

[12]  J. Astola,et al.  Vector median filters , 1990, Proc. IEEE.

[13]  Alfredo Restrepo,et al.  Adaptive trimmed mean filters for image restoration , 1988, IEEE Trans. Acoust. Speech Signal Process..

[14]  JOHN w. WOODS,et al.  Kalman filtering in two dimensions , 1977, IEEE Trans. Inf. Theory.

[15]  Mahmood R. Azimi-Sadjadi,et al.  Two-dimensional recursive parameter identification for adaptive Kalman filtering , 1991 .

[16]  A. Murat Tekalp,et al.  Edge-adaptive Kalman filtering for image restoration with ringing suppression , 1989, IEEE Trans. Acoust. Speech Signal Process..

[17]  Weight selection for the 2-d adaptive weighted median filter based on the gibbs random field model , 1993, IEEE Winter Workshop on Nonlinear Digital Signal Processing.

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

[19]  B. Anderson,et al.  The generation of all q-Markov covers , 1987 .

[20]  P. Bolon.  A family of NL-filters , 1993, IEEE Winter Workshop on Nonlinear Digital Signal Processing.