Piecewise Wiener filter model based on fuzzy partition of local wavelet features for image restoration

Autoregressive Wiener filters are used for prediction and restoration of still frame and video images. Filters of this kind solve a linear optimization problem for the global statistics of an image. They fail when image statistics vary in space (non-stationarity) and when the corrupting noise is nonlinear. A piecewise Wiener filter defined upon a fuzzy partition of the space of local wavelet features is presented and successfully applied to image restoration in the aforementioned cases. Unsupervised clustering of the features using the Bezdek fuzzy c-means algorithm is performed for region estimation and subsequent application of the proper filter h/sub Rk/(n, m) according to a degree of belief /spl mu//sub Rk/. Experimental results indicate increased improvements in signal-to-noise ratios of corrupted images using the proposed method.