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An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy. / Schimmack, Manuel; Mercorelli, Paolo; Georgiadis, Anthimos.
2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016. IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. p. 397 - 402 7575168 (2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Schimmack, M, Mercorelli, P & Georgiadis, A 2016, An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy. in 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016., 7575168, 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 397 - 402, 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdorje, Poland, 29.08.16. https://doi.org/10.1109/MMAR.2016.7575168

APA

Schimmack, M., Mercorelli, P., & Georgiadis, A. (2016). An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy. In 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016 (pp. 397 - 402). Article 7575168 (2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MMAR.2016.7575168

Vancouver

Schimmack M, Mercorelli P, Georgiadis A. An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy. In 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016. IEEE - Institute of Electrical and Electronics Engineers Inc. 2016. p. 397 - 402. 7575168. (2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016). doi: 10.1109/MMAR.2016.7575168

Bibtex

@inbook{36c90539769346b09994af8c55836d6c,
title = "An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy",
abstract = "This paper deals with the noise reduction of discrete AFM surface images using orthogonal wavelets. More in detail, it compares the usefulness of the Daubechies wavelets with different vanishing moments for noise reduction. The work is based upon the discrete wavelet transform (DWT) version of wavelet package transform (WPT). With the help of a seminorm the measurement of noise of a sequence is defined. An algorithm for noise reduction is proposed to detect unavoidable measured noise in topographic surface scans. The denoising wavelet algorithm is used to improve the quality of the scanned images. By taking real measurements in which the measurement and system noise is included, the effectiveness of the proposed denoising algorithm is validated.",
keywords = "Engineering, Atomic Force Microscopy, Discrete wavelet transform method, Noise reduction, Orthogonal wavelets, Surface topography",
author = "Manuel Schimmack and Paolo Mercorelli and Anthimos Georgiadis",
year = "2016",
month = sep,
day = "22",
doi = "10.1109/MMAR.2016.7575168",
language = "English",
series = "2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "397 -- 402",
booktitle = "2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016",
address = "United States",
note = "21st International Conference on Methods and Models in Automation and Robotics (MMAR) ; Conference date: 29-08-2016 Through 01-09-2016",
url = "http://www.ieee-ras.org/component/rseventspro/event/802-mmar-2016-21st-international-conference-on-methods-and-models-in-automation-and-robotics",

}

RIS

TY - CHAP

T1 - An Orthogonal Wavelet Denoising Algorithm for Surface Images of Atomic Force Microscopy

AU - Schimmack, Manuel

AU - Mercorelli, Paolo

AU - Georgiadis, Anthimos

N1 - Conference code: 21

PY - 2016/9/22

Y1 - 2016/9/22

N2 - This paper deals with the noise reduction of discrete AFM surface images using orthogonal wavelets. More in detail, it compares the usefulness of the Daubechies wavelets with different vanishing moments for noise reduction. The work is based upon the discrete wavelet transform (DWT) version of wavelet package transform (WPT). With the help of a seminorm the measurement of noise of a sequence is defined. An algorithm for noise reduction is proposed to detect unavoidable measured noise in topographic surface scans. The denoising wavelet algorithm is used to improve the quality of the scanned images. By taking real measurements in which the measurement and system noise is included, the effectiveness of the proposed denoising algorithm is validated.

AB - This paper deals with the noise reduction of discrete AFM surface images using orthogonal wavelets. More in detail, it compares the usefulness of the Daubechies wavelets with different vanishing moments for noise reduction. The work is based upon the discrete wavelet transform (DWT) version of wavelet package transform (WPT). With the help of a seminorm the measurement of noise of a sequence is defined. An algorithm for noise reduction is proposed to detect unavoidable measured noise in topographic surface scans. The denoising wavelet algorithm is used to improve the quality of the scanned images. By taking real measurements in which the measurement and system noise is included, the effectiveness of the proposed denoising algorithm is validated.

KW - Engineering

KW - Atomic Force Microscopy

KW - Discrete wavelet transform method

KW - Noise reduction

KW - Orthogonal wavelets

KW - Surface topography

UR - http://www.scopus.com/inward/record.url?scp=84991783876&partnerID=8YFLogxK

U2 - 10.1109/MMAR.2016.7575168

DO - 10.1109/MMAR.2016.7575168

M3 - Article in conference proceedings

T3 - 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016

SP - 397

EP - 402

BT - 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st International Conference on Methods and Models in Automation and Robotics (MMAR)

Y2 - 29 August 2016 through 1 September 2016

ER -

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Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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Research output: Journal contributions › Journal articles › Research › peer-review

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Research output: Journal contributions › Journal articles › Research › peer-review

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Research output: Journal contributions › Journal articles › Research › peer-review

Business Analytics and Making Decision Based on Kalman Filter in Stock Prediction Case

Kurniawan, S. & Mercorelli, P., 27.05.2025, 5th Congress on Intelligent Systems, CIS 2024: Volume 1. Kumar, S., Mary Anita, E. A., Kim, J. H. & Nagar, A. (eds.). Springer Science and Business Media Deutschland, p. 309-320 12 p. (Lecture Notes in Networks and Systems; vol. 1275).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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