An orthogonal wavelet denoising algorithm for surface images of atomic force microscopy

Schimmack, Manuel; Mercorelli, Paolo; Georgiadis, Anthimos

doi:10.1109/mmar.2016.7575168

Cited by 1 publication

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“…Moreover, the noise type was detected by analyzing autocorrelation coefficients for different noise [ 35 ]. The effect of some noise errors was defined and reduced [ 36 ] with various methods—for example, correlogram correlation [ 37 , 38 ], Fourier reduction, or random phase exclusion methods [ 39 ], by detecting limits of the roughness tester [ 40 ], limitation and matching of bandwidth for stylus or optical instruments, or the low-noise interference microscope approach [ 41 ], reproducing measurement images with Instrument Transfer Functions (ITFs) or Optical Transfer Functions (OTFs) [ 42 ], some optimization methods [ 43 ] for Coherence Scanning Interferometry measurements, z-axis repeatability studies [ 44 ], an orthogonal wavelet de-noising algorithm [ 45 ], thresholding function [ 46 ], or a comprehensively improved algorithm, which combines wavelet packet decomposition and improved complete ensemble empirical modal decomposition of adaptive noise [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

Podulka

2021

Materials

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The influence of errors in the processes of detection and then reduction of surface topography measurement noise is of great importance; many research papers are concerned with the definition of this type of measurement error. This paper presents the influence of high-frequency measurement noise, defined for various types of surface textures, e.g., two-process plateau-honed, turned, ground, or isotropic. Procedures for the processing of raw measured data as a detection of the high-frequency errors from the results of surface topography measurements were proposed and verified (compared) according to the commonly used (available in the commercial software of the measuring equipment) algorithms. It was assumed that commonly used noise-separation algorithms did not always provide consistent results for two process textures with the valley-extraction analysis; as a result, some free-of-dimple (part of the analyzed detail where dimples do not exist) areas were not carefully considered. Moreover, the influence of measured data processing errors on surface topography parameter calculation was not comprehensively studied with high-frequency measurement noise assessments. It was assumed that the application of the Wavelet Noise Extraction Procedure (WNEP) might be exceedingly valuable when the reduction of a disparate range of measured frequencies (measurement noise) was carefully considered.

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Section: Introductionmentioning

confidence: 99%

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

Podulka

2021

Materials

View full text Add to dashboard Cite

show abstract

An orthogonal wavelet denoising algorithm for surface images of atomic force microscopy

Cited by 1 publication

References 10 publications

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

Reduction of Influence of the High-Frequency Noise on the Results of Surface Topography Measurements

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