Adaptive Choice of the Regularization Parameter in Numerical Differentiation

Adaptive Choice of the Regularization Parameter in Numerical Differentiation

Year:    2015

Author:    Heng Mao

Journal of Computational Mathematics, Vol. 33 (2015), Iss. 4 : pp. 415–427

Abstract

We investigate a novel adaptive choice rule of the Tikhonov regularization parameter in numerical differentiation which is a classic ill-posed problem. By assuming a general unknown Hölder type error estimate derived for numerical differentiation, we choose a regularization parameter in a geometric set providing a nearly optimal convergence rate with very limited a-priori information. Numerical simulation in image edge detection verifies reliability and efficiency of the new adaptive approach.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1503-m2014-0134

Journal of Computational Mathematics, Vol. 33 (2015), Iss. 4 : pp. 415–427

Published online:    2015-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    13

Keywords:    Numerical differentiation Tikhonov regularization Edge detection Adaptive regularization.

Author Details

Heng Mao

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