Nonlinear smoothing filter using adaptive radial clustering
A novel adaptive nonlinear filter is proposed aimed at smoothing homogenous regions while maintaining image structures. The filter can be utilized as a pre-processing tool in image segmentation and edge estimation for improving the results. Several special features are introduced to the filter, including using local adaptive radial clustering and pixel filtering to exclude the influence of outliers and to maintain image structures; using the steepest-ascent method to iteratively update pixels to the nearest clusters obtained by mean-shift; and introducing highly parallel processing by using random seed samples and their associated data blocks which enables fast processing and the global optimum solution of the nonlinear filter. Experiments were done on images of various complexities, and good results were obtained. Evaluations of the filter were also done in terms of edge preservation and image segmentation.