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Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory.

Authors :
LITTLE, MAX A.
JONES, NICK S.
Source :
Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences. 11/ 8/2011, Vol. 467 Issue 2135, p3088-3114. 27p.
Publication Year :
2011

Abstract

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13645021
Volume :
467
Issue :
2135
Database :
Academic Search Index
Journal :
Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences
Publication Type :
Academic Journal
Accession number :
83528360
Full Text :
https://doi.org/10.1098/rspa.2010.0671