A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing

  • T. Preusser
  • , M. Droske
  • , C. S. Garbe
  • , A. Telea
  • , M. Rumpf

    Research output: Contribution to journalArticleAcademicpeer-review

    18 Citations (Scopus)
    481 Downloads (Pure)

    Abstract

    The estimation of optical flow fields from image sequences is incorporated in a Mumford-Shah approach for image denoising and edge detection. Possibly noisy image sequences are considered as input and a piecewise smooth image intensity, a piecewise smooth motion field, and a joint discontinuity set are obtained as minimizers of the functional. The method simultaneously detects image edges and motion field discontinuities in a rigorous and robust way. It is able to handle information on motion that is concentrated on edges. Inherent to it is a natural multiscale approximation that is closely related to the phase. eld approximation for edge detection by Ambrosio and Tortorelli. We present an implementation for two-dimensional image sequences with finite elements in space and time. This leads to three linear systems of equations, which have to be solved in a suitable iterative minimization procedure. Numerical results and different applications underline the robustness of the approach presented.

    Original languageEnglish
    Pages (from-to)599-618
    Number of pages20
    JournalSiam Journal on Applied Mathematics
    Volume68
    Issue number3
    DOIs
    Publication statusPublished - 2007

    Keywords

    • image processing
    • phase field method
    • Mumford-Shah
    • optical flow
    • denoising
    • edge detection
    • segmentation
    • finite element method
    • OPTICAL-FLOW
    • VARIATIONAL APPROACH
    • LEVEL SETS
    • SEGMENTATION
    • REGISTRATION
    • MODELS
    • APPROXIMATION

    Fingerprint

    Dive into the research topics of 'A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing'. Together they form a unique fingerprint.

    Cite this