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Sparse Reconstruction for Microdefect Detection of Two-Dimensional Ultrasound Image Based on Blind Estimation | IEEE Journals & Magazine | IEEE Xplore

Sparse Reconstruction for Microdefect Detection of Two-Dimensional Ultrasound Image Based on Blind Estimation


Abstract:

The sparse reconstruction of two-dimensional (2-D) ultrasound images has proven effective in detecting microdefects in acoustic microimaging (AMI). However, in terms of t...Show More

Abstract:

The sparse reconstruction of two-dimensional (2-D) ultrasound images has proven effective in detecting microdefects in acoustic microimaging (AMI). However, in terms of the acquisition method of a blur kernel for the AMI detection of microdefects, it is difficult for the experimental method to prepare a micrometer-level point source, and the simulation method needs to build different simulation models for different ultrasonic probes. These two methods are troublesome and limit the blur kernel function for sparse reconstruction. This article develops a super-resolution blind estimation algorithm for AMI to generalize the sparse model to different ultrasonic imaging devices and probes. The original blurred image is denoised based on the 2-D sparse representation to perform blur kernel estimation normally. Then, the blur kernel function based on the maximum a posteriori estimation is estimated in the denoised image. We reconstruct the deblurred C-scan images of complex defects with the blur kernel function. The results indicate that the sparse reconstruction for the microdefect detection of a 2-D ultrasound image based on the blind estimation is effective for resolution improvement and signal-to-noise ratio enhancement of microdefect detection.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 68, Issue: 10, October 2021)
Page(s): 10154 - 10161
Date of Publication: 10 September 2020

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