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The Curvelet transform is a higher dimensional generalization of the
Wavelet transform designed to represent images at different scales and
different angles. Curvelets enjoy two unique mathematical properties,
namely:
Curved singularities can be well approximated with very few coefficients
and in a non-adaptive manner - hence the name "curvelets."
Curvelets remain coherent waveforms under the action of the wave equation
in a smooth medium.
More information can be found in the papers below. By releasing
the CurveLab toolbox, we hope to encourage the dissemination of curvelets
to image processing, inverse problems and scientific computing.
The Curvelet.org team: Emmanuel
Candes, Laurent
Demanet, David
Donoho, Lexing
Ying.
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