Ultrasonic image denoising using machine learning in point contact excitation and detection method

A 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 wi...

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Veröffentlicht in:Ultrasonics Jg. 127; S. 106834
Hauptverfasser: Singh, Himanshu, Ahmed, Arif Sheikh, Melandsø, Frank, Habib, Anowarul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.01.2023
Schlagworte:
Autoencoder
Deep learning
point contact excitation and detection
PZT ceramics
Deep learning
point contact excitation and detection
Autoencoder
PZT ceramics
ISSN:0041-624X, 1874-9968, 1874-9968
Online-Zugang:Volltext
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Zusammenfassung:A 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. •Point contact technique is employed to visualize the ultrasonic waves in PZT ceramics•DL-based convolutional autoencoders can be efficient for denoising ultrasonic images•Convolutional autoencoder performed well compared to other noise models
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0041-624X
1874-9968
1874-9968
DOI:10.1016/j.ultras.2022.106834