Denoising Time-Of-Flight Data with Adaptive Total Variation

  • Frank Lenzen
  • Henrik Schäfer
  • Christoph Garbe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

For denoising depth maps from time-of-flight (ToF) cameras we propose an adaptive total variation based approach of first and second order. This approach allows us to take into account the geometric properties of the depth data, such as edges and slopes. To steer adaptivity we utilize a special kind of structure tensor based on both the amplitude and phase of the recorded ToF signal. A comparison to state-of-the-art denoising methods shows the advantages of our approach.

Keywords

Time-of-flight Denoising Adaptive Total Variation Higher Order Regularization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Frank Lenzen
    • 1
    • 2
  • Henrik Schäfer
    • 1
    • 2
  • Christoph Garbe
    • 1
    • 2
  1. 1.Heidelberg Collaboratory for Image ProcessingHeidelberg UniversityGermany
  2. 2.Intel Visual Computing InstituteSaarland UniversityGermany

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