DETERMINISTIC EDGE-PRESERVING REGULARIZATION IN COMPUTED IMAGING

Many image processing problems are ill posed and must be regularized, Usually, a roughness penalty is imposed on the solution, The difficult y is to avoid the smoothing of edges, which are very important attribu tes of the image. In this paper, we first give conditions for the desi gn of such an edge-preserving regularization. Under these conditions, me show that it is possible to introduce an auxiliary variable whose r ole is twofold, First, it marks the discontinuities and ensures their preservation from smoothing, Second, it makes the criterion half-quadr atic. The optimization is then easier, We propose a deterministic stra tegy, based on alternate minimizations on the image and the auxiliary variable, This Leads to the definition of an original reconstruction a lgorithm, called ARTUR, Some theoretical properties of ARTUR are discu ssed, Experimental results illustrate the behavior of the algorithm, T hese results are shown in the field of tomography, but this method can be applied in a large number of applications in image processing.