Multiframe Ultrasonic TOFD Weld Inspection Imaging Based on Wavelet Transform and Image Registration

Abstract

TOFD (time of flight diffraction) is a kind of weld defect detection technology by using ultrasonic diffraction wave signal. Because the diffraction intensity is far less than ultrasonic echo wave intensity, thus, the noise contained in TOFD signal is fairly large, and the formed image is not clear enough. Therefore, it is difficult to determine the size of defects accurately. In this paper, a method of noise reduction of TOFD signal and improving the resolution of the image are discussed based on the combination of wavelet thresholding and image registration. Wavelet multiresolution analysis method is adopted and the A-scan signal is decomposed into different frequency components. We propose a new threshold function to process the wavelet coefficients, which guarantees to denoise while preserving the useful information as much as possible. Setting up the ultrasonic TOFD inspection system and the image data with randomly distributed noise can be obtained via fine shake of the probes during testing. Then, image registration based on maximum correlation and blending is adopted to eliminate the noise in further step. The result shows that the proposed method can achieve denoising, together with resolution enhancement.

Deep Learning-Based Fetal Development Ultrasound Image Segmentation and Registration

“…In order to make an in-depth analysis of the target in the uterus, it is necessary to segment the target from the ultrasound image [7][8][9][10][11][12]. In order to compare the changes of fetal growth index in different periods, it is also necessary to register the fetal development ultrasound images in different periods, even if the corresponding structures of different ultrasound images are consistent in space [13][14][15][16]. Therefore, image segmentation and registration are the premise of ultrasonic image analysis.…”