Statistical methods for noisy images with discontinuities

In this paper, we describe a new statistical method for images which contain discontinuities. The method tries to improve the quality of a ‘measured’ image, which is degraded by the presence of random distortions. This is achieved by using knowledge about the degradation process and a priori information about the main characteristics of the underlying ideal image. Specifically, the method uses information about the discontinuity patterns in small areas of the ‘true’ image. Some auxiliary labels ‘explicitly’ describe the location of discontinuities in the true image. A Bayesian model for the image grey levels and the discontinuity labels is built. The maximum a posteriori estimator is considered. The iterated conditional modes algorithm is used to find a (local) maximum of the posterior distribution. The proposed method has been successfully applied to both artificial and real magnetic resonance images. A comparison of the results with those obtained from three other known methods also has been performed. ...