title: Sparse deconvolution method for ultrasound images based on automatic estimation of reference signals.
abstract: Sparse deconvolution is widely used in the field of non-destructive testing (NDT) for improving the temporal resolution. Generally, the reference signals involved in sparse deconvolution are measured from the reflection echoes of standard plane block, which cannot accurately describe the acoustic properties at different spatial positions. Therefore, the performance of sparse deconvolution will deteriorate, due to the deviations in reference signals. Meanwhile, it is inconvenient for automatic ultrasonic NDT using manual measurement of reference signals. To overcome these disadvantages, a modified sparse deconvolution based on automatic estimation of reference signals is proposed in this paper. By estimating the reference signals, the deviations would be alleviated and the accuracy of sparse deconvolution is therefore improved. Based on the automatic estimation of reference signals, regional sparse deconvolution is achievable by decomposing the whole B-scan image into small regions of interest (ROI), and the image dimensionality is significantly reduced. Since the computation time of proposed method has a power dependence on the signal length, the computation efficiency is therefore improved significantly with this strategy. The performance of proposed method is demonstrated using immersion measurement of scattering targets and steel block with side-drilled holes. The results verify that the proposed method is able to maintain the vertical resolution enhancement and noise-suppression capabilities in different scenarios.
authors.affiliation: The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China.
authors.forename: Haoran
authors.initials: H
authors.lastname: Jin
authors.affiliation: The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China.
authors.forename: Keji
authors.initials: K
authors.lastname: Yang
authors.affiliation: The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China.
authors.forename: Shiwei
authors.initials: S
authors.lastname: Wu
authors.affiliation: The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China.
authors.forename: Haiteng
authors.initials: H
authors.lastname: Wu
authors.affiliation: Ocean College, Zhejiang University, Hangzhou 310027, China. Electronic address: mechenjian@zju.edu.cn.
authors.forename: Jian
authors.initials: J
authors.lastname: Chen
keywords: Reference signal; SpaRSA algorithm; Sparse deconvolution; Ultrasonic B-scan image; Vertical resolution