Nonlinear image representation via local multiscale orientation
Published in:
Technical Report TR2005-875, 27 September 2005
Computer Science Department,
Courant Institute of Mathematical Sciences
New York University
We present a nonlinear image representation based on multiscale local
orientation measurements. Specifically, an image is first decomposed
using a two-orientation steerable pyramid, a tight-frame
representation in which the basis functions are directional
derivatives of a radially symmetric blurring operator. The pair of
subbands at each scale are thus gradients of progressively blurred
copies of the original image. We then discard the magnitude
information and retain only the orientation of each gradient
vector. We develop a method for reconstructing the original image from
this orientation information using an algorithm based on projection
onto convex sets, and demonstrate its robustness to quantization.
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