Wavelet-based decompositions for nonlinear signal processing

Robert D. Nowak, Richard G. Baraniuk

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

2 Scopus citations


Nonlinearities are often encountered in the analysis and processing of real-world signals. This paper develops new signal decompositions for nonlinear analysis and processing. The theory of tensor norms is employed to show that wavelets provide an optimal basis for the nonlinear signal decompositions. The nonlinear signal decompositions are also applied to signal processing problems.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Number of pages12
StatePublished - 1996
EventWavelet Applications in Signal and Image Processing IV - Denver, CO, United States
Duration: Aug 6 1996Aug 6 1996


OtherWavelet Applications in Signal and Image Processing IV
Country/TerritoryUnited States
CityDenver, CO


  • nonlinear signal processing
  • tensor spaces
  • wavelets

ASJC Scopus subject areas

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


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