Multicomposite wavelet estimation

Glenn R. Easley, Demetrio Labate, Vishal M. Patel

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this work, we present a new approach to image denoising derived from the general framework of wavelets with composite dilations. This framework extends the traditional wavelet approach by allowing for waveforms to be defined not only at various scales and locations but also according to various orthogonal transformations such as shearing transformations. The shearlet representation is, perhaps, the most widely known example of wavelets with composite dilations. However, many other representations are obtained within this framework, where directionality properties are controlled by different types of orthogonal matrices, such as the newly defined hyperbolets. In this paper, we show how to take advantage of different wavelets with composite dilations to sparsely represent important features such as edges and texture independently, and apply these techniques to derive improved algorithms for image denoising.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XIV
StatePublished - 2011
EventWavelets and Sparsity XIV - San Diego, CA, United States
Duration: Aug 21 2011Aug 24 2011

Publication series

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


OtherWavelets and Sparsity XIV
Country/TerritoryUnited States
CitySan Diego, CA

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • Wavelets
  • contourlets
  • curvelets
  • directional filter banks
  • shearlets
  • wavelets with composite dilations


Dive into the research topics of 'Multicomposite wavelet estimation'. Together they form a unique fingerprint.

Cite this