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dc.contributor.authorSingh, Himanshu
dc.contributor.authorAhmed, Arif Sheikh
dc.contributor.authorMelandsø, Frank
dc.contributor.authorHabib, Anowarul
dc.date.accessioned2022-12-02T11:17:58Z
dc.date.available2022-12-02T11:17:58Z
dc.date.issued2022-09-06
dc.description.abstractA point contact/Coulomb coupling technique is generally used for visualizing the ultrasonic waves in Lead Zirconate Titanate (PZT) ceramics. The point contact and delta pulse excitation produce a broadband frequency spectrum and wide directional wave vector. In ultrasonic, the signal is corrupted with several types of noises such as speckle, Gaussian, Poisson, and salt and pepper noise. Consequently, the resolution and quality of the images are degraded. The reliability of the health assessment of any civil or mechanical structures highly depends on the ultrasonic signals acquired from the sensors. Recently, deep learning (DL) has been implemented for the reduction of noises from the signals and in images. Here, we have implemented deep learning-based convolutional autoencoders for suitable noise modeling and subsequently denoising the ultrasonic images. Two different metrics, PSNR and SSIM are calculated for quantitative analysis of ultrasonic images. PSNR provides higher visual interpretation, whereas the SSIM can be used to measure much finer similarities. Based upon these parameters speckle-noise demonstrated better than other noise models.en_US
dc.identifier.citationSingh H, Ahmed, Melandsø F, Habib A. Ultrasonic image denoising using machine learning in point contact excitation and detection method. Ultrasonics. 2022;2023(127)en_US
dc.identifier.cristinIDFRIDAID 2067281
dc.identifier.doi10.1016/j.ultras.2022.106834
dc.identifier.issn0041-624X
dc.identifier.issn1874-9968
dc.identifier.urihttps://hdl.handle.net/10037/27666
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalUltrasonics
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleUltrasonic image denoising using machine learning in point contact excitation and detection methoden_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)