ISSN:
1573-1405
Keywords:
scale-space
;
minimal surfaces
;
PDE based non-linear image diffusion
;
selective smoothing
;
color processing
;
texture enhancement
;
movies and volumetric medical data
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract 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 preserving 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, movies, 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 flow for images. Here we show that the geometric flow, based on manifold volume minimization, yields a novel enhancement procedure for color images. We apply the geometric framework and the general Beltrami flow to feature-preserving denoising of images in various spaces. Next, we introduce a new method for color and texture enhancement. Motivated by Gabor's geometric image sharpening method (Gabor, Laboratory Investigation, 14(6):801–807, 1965), we present a geometric sharpening procedure for color images with texture. It is based on inverse diffusion across the multi-channel edge, and diffusion along the edge.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1008171026419
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