Surface Smoothing for Enhancement of 3D Data Using Curvature-Based Adaptive Regularization

  • Hyunjong Ki
  • Jeongho Shin
  • Junghoon Jung
  • Seongwon Lee
  • Joonki Paik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3322)


This paper presents both standard and adaptive versions of regularized surface smoothing algorithms for 3D image enhancement. We incorporated both area decreasing flow and the median constraint as multiple regularization functionals. The corresponding regularization parameters adaptively changes according to the local curvature value. The combination of area decreasing flow and the median constraint can efficiently remove various types of noise, such as Gaussian, impulsive, or mixed types. The adaptive version of the proposed regularized smoothing algorithm changes regularization parameters based on local curvature for preserving local edges and creases that reflects important surface information in 3D data. In addition to the theoretical expansion, experimental results show that the proposed algorithms can significantly enhance 3D data acquired by both laser range sensors and disparity maps from stereo images.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hyunjong Ki
    • 1
  • Jeongho Shin
    • 1
  • Junghoon Jung
    • 1
  • Seongwon Lee
    • 1
  • Joonki Paik
    • 1
  1. 1.Image Processing and Intelligent Systems Lab, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and FilmChung-Ang UniversitySeoulKorea

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