A shearlet approach to edge analysis and detection

  • Authors:

  • Sheng Yi;Demetrio Labate;Glenn R. Easley;Hamid Krim

  • Affiliations:

  • North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;System Planning Corporation, Arlington, VA;North Carolina State University, Raleigh, NC

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2009

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Abstract

It is well known that the wavelet transform provides a very effective framework for analysis of multiscale edges. In this paper, we propose a novel approach based on the shearlet transform: a multiscale directional transform with a greater ability to localize distributed discontinuities such as edges. Indeed, unlike traditional wavelets, shearlets are theoretically optimal in representing images with edges and, in particular, have the ability to fully capture directional and other geometrical features. Numerical examples demonstrate that the shearlet approach is highly effective at detecting both the location and orientation of edges, and outperforms methods based on wavelets as well as other standard methods. Furthermore, the shearlet approach is useful to design simple and effective algorithms for the detection of corners and junctions.