IMAGE-PROCESSING - FLOWS UNDER MIN MAX CURVATURE AND MEAN-CURVATURE/

We present a class of PDE-based algorithms suitable for image denoisin g and enhancement. The techniques are applicable to both salt-and-pepp er gray-scale noise and full-image continuous noise present in black a nd white images, gray-scale images, texture images, and color images. At the core, the techniques rely on two fundamental ideas. First, a le vel set formulation is used for evolving curves; use of this technique to flow isointensity contours under curvature is known to remove nois e and enhance images. Second, the particular form of the curvature how is governed by a minimax switch which selects a range of denoising de pendent on the size of switching window. Our approach has several virt ues. First, it contains only one enhancement parameter, which in most cases is automatically chosen. Second, the scheme automatically stops smoothing at a point which depends on the switching window size; conti nued application of the scheme produces no further change. Third, the method is one of the fastest possible schemes based on a curvature-con trolled approach. (C) 1996 Academic Press, Inc.