Efficient Beltrami flow in patch-space

Aaron Wetzler, Ron Kimmel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The Beltrami framework treats images as two dimensional manifolds embedded in a joint features-space domain. This way, a color image is considered to be a two dimensional surface embedded in a hybrid special-spectral five dimensional {x, y, R, G, B} space. Image selective smoothing, often referred to as a denoising filter, amounts to the process of area minimization of the image surface by mean curvature flow. One interesting variant of the Beltrami framework is treating local neighboring pixels as the feature-space. A distance is defined by the amount of deformation a local patch undergoes while traversing its support in the spatial domain. The question we try to tackle in this note is how to perform patch based denoising accurately, and efficiently. As a motivation we demonstrate the performance of the Beltrami filter in patch-space, and provide useful implementation considerations that allow for parameter tuning and efficient implementation on hand-held devices like smart phones.

Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision - Third International Conference, SSVM 2011, Revised Selected Papers
Number of pages10
StatePublished - 2012
Event3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011 - Ein-Gedi, Israel
Duration: 29 May 20112 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6667 LNCS


Conference3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011


  • Beltrami flow
  • denoising
  • patch-space

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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