%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 }