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A General Framework for Bilateral and Mean Shift Filtering

J. Solomon,Keenan Crane,Adrian Butscher,C. Wojtan

Published 2014 in arXiv.org

ABSTRACT

We present a generalization of the bilateral filter that can be applied to feature-preserving smoothing of signals on images, meshes, and other domains within a single unified framework. Our discretization is competitive with state-of-the-art smoothing techniques in terms of both accuracy and speed, is easy to implement, and has parameters that are straightforward to understand. Unlike previous bilateral filters developed for meshes and other irregular domains, our construction reduces exactly to the image bilateral on rectangular domains and comes with a rigorous foundation in both the smooth and discrete settings. These guarantees allow us to construct unconditionally convergent mean-shift schemes that handle a variety of extremely noisy signals. We also apply our framework to geometric edge-preserving effects like feature enhancement and show how it is related to local histogram techniques.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    arXiv.org

  • Publication date

    2014-04-30

  • Fields of study

    Mathematics, Computer Science, Engineering

  • Identifiers
    arXiv 1405.4734
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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