| Ultrasound imaging technology is widely used in medical clinical diagnosis and industrial non-destructive testing due to its advantages of good real-time,low cost,noninvasive,non-ionizing radiation and painless.Using synthetic aperture technology,increasing the sampling frequency and other methods to increase the amount of received echo data can improve the imaging quality,but too much data can make it difficult for an ultrasound system to process,thus increasing the complexity of the ultrasonic system.Compressed sensing technology can solve the problems of big data acquisition,transmission and storage in ultrasonic hardware system,and can reconstruct the signal from a small amount of data with high accuracy.However,two problems remain.First,the construction of measurement matrix is one of the key steps in the process of compressed sensing.However,the random measurement matrix has a complex structure,which requires large storage space and is difficult for hardware implementation.Although the structure of the commonly used deterministic measurement matrix is relatively simple,but when the sampling rate is low,the storage resource is wasted.Moreover,due to its structural characteristics,the effect of image reconstruction is poor for overlapping ultrasonic signals.Secondly,the traditional sparse representation process does not consider the structural characteristics of the data,and it is lack of pertinence for the overlapped ultrasonic echo,so the effect of sparse representation is poor and the quality of reconstructed image is poor.In view of the above two problems,this paper carries out the following research:(1)In this paper,three simple and efficient measurement matrices are proposed and applied to ultrasonic imaging algorithm for verification: orthogonal baseline representation matrix OBLR,deterministic binary sparse block diagonal matrix BSBD and measurement matrix DRS constructed from deterministic random sequences.The OBLR matrix combines diagonal matrix and orthogonal baseline representation principle.The design method is linearized.The matrix has the following advantages: simple and flexible design process,high speed,high sparsity,no redundancy.The BSBD matrix is composed of binary block matrices arranged diagonally,and all elements are "0" except for "1" in binary block matrix.The matrix has the following advantages: simple structure,high sparsity,and easy hardware implementation;no good correlation with the commonly used sparse dictionary;high measurement efficiency.The DRS matrix is composed of a random sequence generated by the explicit function of Logistic mapping.The sequence is generated by a certain mathematical structure,and the sequence itself has a certain randomness.The matrix has the following advantages: easy transmission and storage;high measurement efficiency.The experimental results show that: in the reconstruction of ultrasonic signal,compared with several common measurement methods,the reconstruction error of the three measurement matrix is small,the peak signal-to-noise ratio is large,and the ultrasonic image can be reconstructed with high accuracy at 30%sampling rate.(2)A sparse dictionary BOCC based on Chirp code is proposed and applied to ultrasonic imaging algorithm for verification.BOCC uses the coded pulse signal to design the basis function,which makes full use of the characteristic that the ultrasonic echo signal is superimposed after the attenuation of the transmitted signal.The dictionary has strong sparse representation ability for ultrasonic signal,which reduces the complexity of ultrasonic hardware system,and the coding excitation technology can be implemented at a low cost In order to improve the signal-to-noise ratio of ultrasound imaging under the amount of sampling data.The experimental results show that compared with several common sparse representations,BOCC sparse dictionary has the least reconstruction error and the best image quality,and the ultrasonic image can be reconstructed with high accuracy at 30% sampling rate. |
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