Abstract

This paper is concerned with robust models for representing images. The robust methods in image models are also applied to some important image processing situations such as segmentation by texture and image restoration in the presence of outliers. We consider a non-symmetric half plane (NSHP) autoregressive image model, where the image intensity at a point is a linear combination of the intensities of the eight nearest points located on one quadrant of the coordinate plane, plus an innovation process. Robust estimation algorithms for different outlier processes in causal autoregressive models are developed. These algorithms are based on robust generalized M (GM) estimators. Theoretical properties of the robust estimation algorithms are presented. The robust estimation algorithm for causal autoregressive models is applied to image restoration. The restoration method based on robust image model cleans out the outliers without involving any blurring of the image. Experimental results show that the quality of images restored by the model-based method is superior to the images restored by other conventional methods. © 2001 Elsevier Science B.V. All rights reserved.