This work presents a novel bond percolation-based approach to determine the clique potential parameters of a Gibbs-Markov model as a function of local characteristics of the underlying image. Using the renormalization group transformation, a multiscale description of the clique potential parameters is formed and used to obtain multiresolution image segmentation.

A new bond percolation-based approach is presented to determine the clique potential parameters of a Gibbs-Markov random field (GMRF) model used in image segmentation. Previously, experimentally determined fixed values were used for these parameters independent of the underlying image. Using the proposed approach, these parameters are now derived as a function of local characteristics of the image under consideration. An additional salient feature of this method is its suitability for a renormalization group approach to multi-scale description of the clique potential parameters.>

One of the most commonly used image enhancement techniques is noise removal. The goal of a noise removal method is to remove noise while preserving the image structure. In this paper a segmentation-based image enhancement method is presented which confines image smoothing to the region interiors. Since the structure in the image is captured by segmentation, confining image smoothing to the region interiors does not cause any significant loss of this structure. In fact, region confined averaging enhances the contrast between boundaries, and hence sharpens the image at these boundaries. The result is an image with reduced noise, but that appears sharper than the original.>

The authors describe a human visual perception approach and report some results for this problem. The approach is based on the differential quantization of images, in which smooth approximations are subtracted from the image prior to quantization. They consider two such approximations. The first one is an approximation by splines obtained from a sparse and fixed subsampled array of the image. The second one segments the image into piecewise constant regions on the basis of the local activity of the image. Both these approximations result in remainders of residual images where large errors are localized in portions of the image of high activity. Because of visual masking the remainder image can now be coarsely quantized without visual impairment to the reconstructed image. The coarsely quantized remainder is now encoded in an error free manner. In such a perceptually based encoding method the mean square error is now dependent on the activity of the image.>