Curvelet transform with adaptive tiling

Hasan Al-Marzouqi, Ghassan AlRegib

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

6 Scopus citations

Abstract

The curvelet transform is a recently introduced non-adaptive multi-scale transform that have gained popularity in the image processing field. In this paper, we study the effect of customized tiling of frequency content in the curvelet transform. Specifically, we investigate the effect of the size of the coarsest level and its relationship to denoising performance. Based on the observed behavior, we introduce an algorithm to automatically choose the optimal number of decompositions. Its performance shows a clear advantage, in denoising applications, when compared to default curvelet decomposition. We also examine how denoising is affected by varying the number of divisions per scale.

Original languageBritish English
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Image Processing
Subtitle of host publicationAlgorithms and Systems X; and Parallel Processing for Imaging Applications II
DOIs
StatePublished - 2012
EventImage Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II - Burlingame, CA, United States
Duration: 23 Jan 201225 Jan 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8295
ISSN (Print)0277-786X

Conference

ConferenceImage Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Country/TerritoryUnited States
CityBurlingame, CA
Period23/01/1225/01/12

Keywords

  • curvelet transform
  • denoising, transform theory
  • image processing
  • wavelet extension

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