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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> estimation is estimated in the denoised image. We reconstruct the deblurred <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> -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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.