Noise is ubiquitous in nature; it decreases the measurement or data
reliability. This reliability may be increased by using a novel method
that detects and reduces noise for preserving signals, called the
Rolling-circle method. Our method treats noisy signals by eroding
finite-difference moduli, circle radii, with either a version for
denoising signals or a version for smoothing signals. Both versions were
compared to a moving average filter, a simple median filter, an adaptive
median filter, a Savitzky-Golay filter, and two wavelet threshold
filters. For these filters, we tested four cases: denoising a histogram,
denoising a Raman spectrum, smoothing a well-log, and denoising a
synthetic signal. In such cases, our method has surpassed the mentioned
filters as measured through well-known metrics. A quantitative metric,
called 0-metric, is also proposed in this work for assessing the amount
of preserved data. The preservation of a clean signal was only achieved
entirely by the proposed method, which we believe that no filter ever
did thus far. (C) 2020 Elsevier Inc. All rights reserved.
%0 Journal Article
%1 WOS:000539114000004
%A Vieira, Rafael S T
%A Teixeira, Daniel N
%A Cavalcante-Neto, Joaquim B
%A Machado, Javam C
%A Filho, Francisco N
%C 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA
%D 2020
%I ACADEMIC PRESS INC ELSEVIER SCIENCE
%J DIGITAL SIGNAL PROCESSING
%K Absolute Denoising; Noise Signal detection; erosion} finite-difference preservation; smoothing; {Signal
%R 10.1016/j.dsp.2020.102764
%T Rolling-circles: Detect and reduce noise via erosion of
finite-difference moduli for preserving signals
%V 103
%X Noise is ubiquitous in nature; it decreases the measurement or data
reliability. This reliability may be increased by using a novel method
that detects and reduces noise for preserving signals, called the
Rolling-circle method. Our method treats noisy signals by eroding
finite-difference moduli, circle radii, with either a version for
denoising signals or a version for smoothing signals. Both versions were
compared to a moving average filter, a simple median filter, an adaptive
median filter, a Savitzky-Golay filter, and two wavelet threshold
filters. For these filters, we tested four cases: denoising a histogram,
denoising a Raman spectrum, smoothing a well-log, and denoising a
synthetic signal. In such cases, our method has surpassed the mentioned
filters as measured through well-known metrics. A quantitative metric,
called 0-metric, is also proposed in this work for assessing the amount
of preserved data. The preservation of a clean signal was only achieved
entirely by the proposed method, which we believe that no filter ever
did thus far. (C) 2020 Elsevier Inc. All rights reserved.
@article{WOS:000539114000004,
abstract = {Noise is ubiquitous in nature; it decreases the measurement or data
reliability. This reliability may be increased by using a novel method
that detects and reduces noise for preserving signals, called the
Rolling-circle method. Our method treats noisy signals by eroding
finite-difference moduli, circle radii, with either a version for
denoising signals or a version for smoothing signals. Both versions were
compared to a moving average filter, a simple median filter, an adaptive
median filter, a Savitzky-Golay filter, and two wavelet threshold
filters. For these filters, we tested four cases: denoising a histogram,
denoising a Raman spectrum, smoothing a well-log, and denoising a
synthetic signal. In such cases, our method has surpassed the mentioned
filters as measured through well-known metrics. A quantitative metric,
called 0-metric, is also proposed in this work for assessing the amount
of preserved data. The preservation of a clean signal was only achieved
entirely by the proposed method, which we believe that no filter ever
did thus far. (C) 2020 Elsevier Inc. All rights reserved.},
added-at = {2022-05-23T20:00:14.000+0200},
address = {525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA},
author = {Vieira, Rafael S T and Teixeira, Daniel N and Cavalcante-Neto, Joaquim B and Machado, Javam C and Filho, Francisco N},
biburl = {https://www.bibsonomy.org/bibtex/20f5beed53921bd1c5199223ded3ba306/ppgfis_ufc_br},
doi = {10.1016/j.dsp.2020.102764},
interhash = {437f4c449df8fdec0192bd9ef2a8b604},
intrahash = {0f5beed53921bd1c5199223ded3ba306},
issn = {1051-2004},
journal = {DIGITAL SIGNAL PROCESSING},
keywords = {Absolute Denoising; Noise Signal detection; erosion} finite-difference preservation; smoothing; {Signal},
publisher = {ACADEMIC PRESS INC ELSEVIER SCIENCE},
pubstate = {published},
timestamp = {2022-05-23T20:00:14.000+0200},
title = {Rolling-circles: Detect and reduce noise via erosion of
finite-difference moduli for preserving signals},
tppubtype = {article},
volume = 103,
year = 2020
}