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Please use this identifier to cite or link to this item: http://hdl.handle.net/1903/8664

Title: RECOVERY OF EDGES FROM SPECTRAL DATA WITH NOISE—A NEW PERSPECTIVE
Authors: ENGELBERG, SHLOMO
TADMOR, EITAN
Type: Article
Keywords: piecewise smoothness
edge detection
noisy data
concentration kernels
constrained optimization
separation of scales
Issue Date: 2008
Publisher: Copyright: Society for Industrial and Applied Mathematics
Citation: S. Engelberg & E. Tadmor (2008). Recovery of edges from spectral data with noise---a new perspective. SIAM Journal on Numerical Analysis 46(5) (2008), 2620-2635.
Abstract: We consider the problem of detecting edges—jump discontinuities in piecewise smooth functions from their N-degree spectral content, which is assumed to be corrupted by noise. There are three scales involved: the “smoothness” scale of order 1/N, the noise scale of order √η, and the O(1) scale of the jump discontinuities. We use concentration factors which are adjusted to the standard deviation of the noise √η  1/N in order to detect the underlying O(1)-edges, which are separated from the noise scale √η  1.
URI: http://hdl.handle.net/1903/8664
Appears in Collections:Mathematics Research Works

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