Abstract
In this chapter the family of functions, called smoothlets, was presented. A smoothlet is defined as a generalization of a wedgelet and a second order wedgelet. It is based on any curve beamlet, named as a curvilinear beamlet. Smoothlets, unlike the other adaptive functions, are continuous functions. Thanks to that they can adapt to edges of different blur. In more details, the smoothlet can adapt to location, scale, orientation, curvature and blur. Additionally, a sliding smoothlet was introduced. It is the smoothlet with location and size defined freely within an image. The Rate-Distortion dependency and the \(\mathcal {M}\)-term approximation of smoothlets were also discussed.
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Lisowska, A. (2014). Smoothlets. In: Geometrical Multiresolution Adaptive Transforms. Studies in Computational Intelligence, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-319-05011-9_2
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DOI: https://doi.org/10.1007/978-3-319-05011-9_2
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