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Rapid Anisotropic Diffusion Using Space-Variant Vision

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Abstract

Many computer and robot vision applications require multi-scale image analysis. Classically, this has been accomplished through the use of a linear scale-space, which is constructed by convolution of visual input with Gaussian kernels of varying size (scale). This has been shown to be equivalent to the solution of a linear diffusion equation on an infinite domain, as the Gaussian is the Green's function of such a system (Koenderink, 1984). Recently, much work has been focused on the use of a variable conductance function resulting in anisotropic diffusion described by a nonlinear partial differential equation (PDE). The use of anisotropic diffusion with a conductance coefficient which is a decreasing function of the gradient magnitude has been shown to enhance edges, while decreasing some types of noise (Perona and Malik, 1987). Unfortunately, the solution of the anisotropic diffusion equation requires the numerical integration of a nonlinear PDE which is a costly process when carried out on a uniform mesh such as a typical image. In this paper we show that the complex log transformation, variants of which are universally used in mammalian retino-cortical systems, allows the nonlinear diffusion equation to be integrated at exponentially enhanced rates due to the nonuniform mesh spacing inherent in the log domain. The enhanced integration rates, coupled with the intrinsic compression of the complex log transformation, yields a speed increase of between two and three orders of magnitude, providing a means of performing rapid image enhancement using anisotropic diffusion.

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Authors and Affiliations

  1. Department of Cognitive and Neural Systems, Boston University, Boston, MA, 02215. E-mail

    Bruce Fischl, Michael A. Cohen & Eric L. Schwartz

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  1. Bruce Fischl
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  2. Michael A. Cohen
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Fischl, B., Cohen, M.A. & Schwartz, E.L. Rapid Anisotropic Diffusion Using Space-Variant Vision. International Journal of Computer Vision 28, 199–212 (1998). https://doi.org/10.1023/A:1008043919667

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