IEEE Transactions on Industrial Electronics, pp. 1–1, Sep. 2020, DOI: 10.1109/TIE.2020.3021651

Sparse Reconstruction for Micro-defect Detection of Two-dimensional Ultrasound Image Based on Blind Estimation

Lei Su1, Xiaonan Yu1, Ke Li1, Jiefei Gu1 and Michael G. Pecht2
1 Jiangnan University, 66374 Wuxi, Jiangsu China
2 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA


Sparse reconstruction of two-dimensional ultrasound images has proven effective in detecting micro- defects in acoustic micro imaging (AMI). However, in terms of the acquisition method of a blur kernel for AMI detection of micro-defects, it is difficult for the experimental method to prepare micron-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 paper 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 two-dimensional sparse representation to perform blur kernel estimation normally. Then, the blur kernel function based on the maximum a posterior 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 micro-defect detection of a two-dimensional ultrasound image based on blind estimation is effective for resolution improvement and signal-to-noise ratio enhancement of micro-defect detection.

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