Piecewise Wiener filter model based on fuzzy partition of local wavelet features for image restoration
Ioannis M. Stephanakis, Giorgos B. Stamou, Stefanos Kollias · 2003
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.