Abstract
This chapter introduces a number of systems of coupled, nonlinear diffusion equations and investigates their role in noise suppression and edge-preserving smoothing. The basic idea is that several maps describing the image, undergo coupled development towards an equilibrium state, repre- senting the enhanced image. These maps could e.g. contain intensity, local edge strength, range, or another quantity. All these maps, including the edge map, contain continuous rather than all-or-nothing information, following a strategy of least commitment. Each of the approaches has been developed and tested on a parallel transputer network.
Post-doctoral Research Fellow of the Belgian National Fund for Scientific Research (NFWO).
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© 1994 Springer Science+Business Media Dordrecht
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Proesmans, M., Pauwels, E., van Gool, L. (1994). Coupled Geometry-Driven Diffusion Equations for Low-Level Vision. In: ter Haar Romeny, B.M. (eds) Geometry-Driven Diffusion in Computer Vision. Computational Imaging and Vision, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1699-4_9
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DOI: https://doi.org/10.1007/978-94-017-1699-4_9
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4461-7
Online ISBN: 978-94-017-1699-4
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