Image Processing: Flows under Min/Max Curvature and Mean Curvature

We present a class of PDE-based algorithms suitable for image denoising and enhancement. The techniques are applicable to both salt-and-pepper gray-scale noise and full-image continuous noise present in black and white images, gray-scale images, texture images, and color images. At the core, the techniques rely on two fundamental ideas. First, a level set formulation is used for evolving curves; use of this technique to flow isointensity contours under curvature is known to remove noise and enhance images. Second, the particular form of the curvature flow is governed by a min/max switch which selects a range of denoising dependent on the size of switching window. Our approach has several virtues. 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; continued application of the scheme produces no further change. Third, the method is one of the fastest possible schemes based on a curvature-controlled approach.