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<item>
  <id>06078417</id>
  <dt>a</dt>
  <an>06078417</an>
  <augroup>
    <au>Sauer, Tomas</au>
  </augroup>
  <ti>Shearlet multiresolution and multiple refinement.</ti>
  <so>Kutyniok, Gitta (ed.) et al., Shearlets. Multiscale analysis for multivariate data. Boston, MA: Birkh\"auser (ISBN 978-0-8176-8315-3/hbk; 978-0-8176-8316-0/ebook). Applied and Numerical Harmonic Analysis, 199-237 (2012).</so>
  <py>2012</py>
  <pu>Boston, MA: Birkh\"auser</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
    <ut>filterbank</ut>
    <ut>multiresolution analysis</ut>
    <ut>multiple MRA</ut>
    <ut>shearlet MRA</ut>
    <ut>subdivision</ut>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1007/978-0-8176-8316-0_6</li>
  </ligroup>
  <abgroup>
    <ab>Summary: Starting from the concept of filterbanks and subband coding, we present an entirely digital approach to shearlet multiresolution which is not a discretization of the continuous transform but is naturally connected to the filtering of discrete data, the usual procedure in digital signal processing. It will be shown that a full analogy of multiresolution analysis (MRA) can be derived also for shearlets as a special instance of multiple MRA (MMRA) based on cascading a finite number of filterbanks. In this discrete shearlet transform, the MMRA concept goes hand in hand with shear-based scaling matrices of a particularly appealing and simple geometry. Finally, also some application issues of such discrete transformations will be considered briefly.</ab>
    <rv></rv>
  </abgroup>
</item>