Images as embedded maps and minimal surfaces: Movies, color, texture, and volumetric medical images

We extend the geometric framework introduced in Sochen et al. (IEEE Trans. on Image Processing, 7(3):310-318, 1998) for image enhancement. We analyze and propose enhancement techniques that selectively smooth images while pre serving either the multi-channel edges or the orientation-dependent texture features in them. Images are treated as manifolds in a feature-space. This geometrical interpretation lead to a general way for grey level, color, mo vies, volumetric medical data, and color-texture image enhancement. We first review our framework in which the Polyakov action from high-energy physics is used to develop a minimization procedure through a geometric fl ow for images. Here we show that the geometric flow, based on manifold volu me minimization, yields a novel enhancement procedure for color images. We apply the geometric framework and the general Beltrami flow to feature-pres erving denoising of images in various spaces. Next, we introduce a new method for color and texture enhancement. Motivate d by Gabor's geometric image sharpening method (Gabor, Laboratory Investiga tion, 14(6):801-807, 1965), we present a geometric sharpening procedure for color images with texture. It is based on inverse diffusion across the mul ti-channel edge, and diffusion along the edge.