Robust anisotropic diffusion and sharpening of scalar and vector images

Michael Black, Guillermo Sapiro, David Marimont, David Heeger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Relations between anisotropic diffusion and robust statistics are described in this paper. We show that anisotropic diffusion can be seen as a robust estimation procedure that estimates a piecewise smooth image from a noisy input image. The `edge-stopping' function in the anisotropic diffusion equation is closely related to the error norm and influence function in the robust estimation framework. This connection leads to a new `edge-stopping' function based on Tukey's biweight robust estimator, that preserves sharper boundaries than previous formulations and improves the automatic stopping of the diffusion. The robust statistical interpretation also provides a means for detecting the boundaries (edges) between the piecewise smooth regions in the image. We extend the framework to vector-valued images and show applications to robust image sharpening.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
PublisherIEEE Comp Soc
Pages263-266
Number of pages4
Volume1
StatePublished - 1997
EventProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3) - Santa Barbara, CA, USA
Duration: Oct 26 1997Oct 29 1997

Other

OtherProceedings of the 1997 International Conference on Image Processing. Part 2 (of 3)
CitySanta Barbara, CA, USA
Period10/26/9710/29/97

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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  • Cite this

    Black, M., Sapiro, G., Marimont, D., & Heeger, D. (1997). Robust anisotropic diffusion and sharpening of scalar and vector images. In IEEE International Conference on Image Processing (Vol. 1, pp. 263-266). IEEE Comp Soc.