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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Sparse Reconstruction for Microdefect Detection of Two-Dimensional Ultrasound Image Based on Blind Estimation
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Sparse Reconstruction for Microdefect Detection of Two-Dimensional Ultrasound Image Based on Blind Estimation

机译：基于盲估计的二维超声图像微碎片检测稀疏重建

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摘要

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.
机译：二维（2-D）超声图像的稀疏重建已经有效地检测声学微观（AMI）中的微碎片。然而，就AMI检测的模糊核的采集方法而言，实验方法难以制备千分尺寸点源，并且模拟方法需要为不同的超声波探头构建不同的仿真模型。这两种方法是麻烦的，并限制模糊核心稀疏重建的核心功能。本文开发了一种用于AMI的超分辨率盲估计算法，以将稀疏模型概括为不同的超声成像装置和探针。原始模糊图像基于2-D稀疏表示，以正常执行模糊内核估计。然后，基于最大后估计的模糊内核功能估计在去噪图像中。我们用模糊内核功能重建复杂缺陷的解束C扫描图像。结果表明，基于盲估计的二维超声图像微碎片检测的稀疏重建对于分辨率提高和信噪比来有效地提高微碎屑检测。

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