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Books on the topic 'Denoising'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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1

Mourad, Talbi. ECG Denoising Based on Total Variation Denoising and Wavelets. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25267-9.

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2

Escalera, Sergio, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, and Xavier Baró, eds. Inpainting and Denoising Challenges. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25614-2.

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3

Bertalmío, Marcelo, ed. Denoising of Photographic Images and Video. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96029-6.

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4

Shukla, K. K. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. London: Springer London, 2013.

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5

Tiwari, R. K., and R. Rekapalli. Modern Singular Spectral-Based Denoising and Filtering Techniques for 2D and 3D Reflection Seismic Data. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-19304-1.

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6

Paul, Sabyasachi. Use of wavelet based iterative filtering to improve denoising of spectral information for in-vivo gamma spectrometry. Mumbai, India: Bhabha Atomic Research Centre, 2012.

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7

Pham, Tuan Van. Wavelet analysis for robust speech processing and applications: Applications of discrete wavelet transform and wavelet denoising to speech enhancement and robust speech recognition. Saarbrücken: VDM, Verlag Dr. Müller, 2008.

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8

ECG Denoising Based on Total Variation Denoising and Wavelets. Springer International Publishing AG, 2023.

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9

Wan, Jun, Sergio Escalera, Xavier Baró, Stephane Ayache, Meysam Madadi, and Umut Güçlü. Inpainting and Denoising Challenges. Springer, 2019.

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10

Wan, Jun, Sergio Escalera, Stephane Ayache, Meysam Madadi, and Umut Güçlü. Inpainting and Denoising Challenges. Springer International Publishing AG, 2020.

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11

Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.

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12

Denoising - New Insights [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.102307.

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13

Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.

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14

Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Limited, John, 2022.

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15

Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.

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16

Ouahabi, Abdeljalil. Wavelet Denoising: Application in Medical Imaging. Wiley & Sons, Incorporated, John, 2019.

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17

Ouahabi, Abdeljalil. Wavelet Denoising: Application in Medical Imaging. Wiley & Sons, Incorporated, John, 2019.

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18

Ouahabi, Abdeljalil. Wavelet Denoising: Application in Medical Imaging. Wiley & Sons, Incorporated, John, 2019.

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19

Signal Enhancement Using Time-Frequency Based Denoising. Storming Media, 2003.

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20

Denoising of Ocean Acoustic Signals using Wavelet-Based Techniques. Storming Media, 1996.

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21

Optimal Wavelet Denoising for High Range Resolution Radar Classification. Storming Media, 1999.

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22

Denoising of Acoustic Signals Using Wavelet/Wiener Based Techniques. Storming Media, 1998.

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23

Time Difference of Arrival (TDOA) Estimation Using Wavelet Based Denoising. Storming Media, 1999.

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24

Bertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.

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25

Bertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.

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26

Shukla, K. K., and Arvind K. Tiwari. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. Springer London, Limited, 2013.

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27

Localization of Wireless Emitters Based on the Time Difference of Arrival (TDOA) and Wavelet Denoising. Storming Media, 1999.

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28

Rekapalli, R., and R. K. Tiwari. Modern Singular Spectral-Based Denoising and Filtering Techniques for 2D and 3D Reflection Seismic Data. Springer, 2020.

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29

Congendo, Marco, and Fernando H. Lopes da Silva. Event-Related Potentials. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0039.

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Event-related potentials (ERPs) can be elicited by a variety of stimuli and events in diverse conditions. This chapter covers the methodology of analyzing and quantifying ERPs in general. Basic models (additive, phase modulation and resetting, potential asymmetry) that account for the generation of ERPs are discussed. The principles and requirements of ensemble time averaging are presented, along with several univariate and multivariate methods that have been proposed to improve the averaging procedure: wavelet decomposition and denoising, spatial, temporal and spatio-temporal filtering. We emphasize basic concepts of principal component analysis, common spatial pattern, and blind source separation, including independent component analysis. We cover practical questions related to the averaging procedure: overlapping ERPs, correcting inter-sweep latency and amplitude variability, alternative averaging methods (e.g., median), and estimation of ERP onset. Some specific aspects of ERP analysis in the frequency domain are surveyed, along with topographic analysis, statistical testing, and classification methods.
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30

Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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