Nonlinear adaptive image filtering based on inhomogeneous diffusion and differential geometry

Adel I. El-Fallah; Gary E. Ford

doi:10.1117/12.171091

23 March 1994 Nonlinear adaptive image filtering based on inhomogeneous diffusion and differential geometry

Adel I. El-Fallah, Gary E. Ford

Proceedings Volume 2182, Image and Video Processing II; (1994) https://doi.org/10.1117/12.171091
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States

Abstract

The inadequacy of the classic linear approach to edge detection and scale space filtering lies in the spatial averaging of the Laplacian. The Laplacian is the divergence of the gradient and thus is the divergence of both magnitude and direction. The divergence in magnitude characterizes edges and this divergence must not be averaged if the image structure is to be preserved. We introduce a new nonlinear filtering theory that only averages the divergence of direction. This averaging keeps edges and lines intact as their direction is nondivergent. Noise does not have this nondivergent consistency and its divergent direction is averaged. Higher order structures such as corners are singular points or inflection points in the divergence of direction and also are averaged. Corners are intersection points of edges of nondivergent direction (or smooth curves of small divergence in direction) and their averaging is limited. This approach provides a better compromise between noise removal and preservation of image structure. Experiments that verify and demonstrate the adequacy of this new theory are presented.

Citation Download Citation

Adel I. El-Fallah and Gary E. Ford "Nonlinear adaptive image filtering based on inhomogeneous diffusion and differential geometry", Proc. SPIE 2182, Image and Video Processing II, (23 March 1994); https://doi.org/10.1117/12.171091

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