Anne Gelb

Detection of Edges in Spectral Data II. Nonlinear Enhancement

We discuss a general framework for recovering edges in piecewise smooth functions with finitely many jump discontinuities, where

A method to reduce the Gibbs ringing artifact in MRI scans while keeping tissue boundary integrity

[f](x):=f(x+)-f(x-) \neq 0

Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images

. Our approach is based on two main aspects--- localization using appropriate concentration kernels and separation of scales by nonlinear enhancement. To detect such edges, one employs concentration kernels,

Enhanced spectral viscosity approximations for conservation laws

K_\epsilon(\cdot)

Improved Total Variation-Type Regularization Using Higher Order Edge Detectors

, depending on the small scale

Adaptive Edge Detectors for Piecewise Smooth Data Based on the minmod Limiter

\epsilon

Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method

. It is shown that odd kernels, properly scaled, and admissible (in the sense of having small

Modelling annular micromixers

W^{-1,\infty}

The resolution of the Gibbs phenomenon for spherical harmonics

-moments of order

Parameter Optimization and Reduction of Round Off Error for the Gegenbauer Reconstruction Method

{\cal O}(\epsilon)