Abstract
We present a framework for enhancing images while preserving either the edge or the orientation-dependent texture information present in them. We do this by treating images as manifolds in a feature-space. This geometrical interpretation leads to a natural way for grey level, color, movies, volumetric medical data, and color-texture image enhancement. Following this, we invoke the Polyakov action from high-energy physics, and develop a minimization procedure through a geometric flow. This flow, based on manifold volume minimization yields a natural enhancement procedure. We apply this framework to edge-preserving denoising of grey value and color images, for volumetric medical data, and orientation-preserving flows for grey level and color texture images.
This work is supported in part by the Applied Mathematics Subprogram of the Office of Energy Research under DE-AC03-76SF00098, ONR grant under N00014-961-0381, and in part by the National Science Foundation under grant PHY-90-21139.
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Kimmel, R., Malladi, R., Sochen, N. (1997). Image processing via the beltrami operator. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_169
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DOI: https://doi.org/10.1007/3-540-63930-6_169
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