CN115372473A - Self-adaptive ultrasonic image artifact removing method based on SoS function modulation - Google Patents

Abstract

The invention discloses a self-adaptive ultrasonic image artifact removing method based on SoS function modulation, which comprises the steps of modulating an ultrasonic echo signal measured in conventional ultrasonic detection by using a SoS function to obtain a transform domain signal, sampling the modulated transform domain signal at an equal interval and a low rate to obtain a discrete sequence value of the modulated signal, performing discrete Fourier transform on the discrete sequence to obtain a Fourier coefficient sequence of an original ultrasonic echo signal, estimating parameters of the ultrasonic echo signal by using a spectrum estimation algorithm to obtain characteristic parameters of an ultrasonic detection signal, performing self-adaptive waveform reconstruction of the echo signal by combining an ultrasonic sensor waveform, and performing acoustic imaging by using the reconstructed signal to obtain an ultrasonic pseudo-color image without artifacts. The method effectively eliminates the artifacts of the ultrasonic image, reserves the information of the position, the shape, the size and the like of the defects, is suitable for industrial ultrasonic imaging, and has good application prospect.

Description

An Adaptive Ultrasonic Image Artifact Removal Method Based on SoS Function Modulation

technical field

The invention relates to the technical field of nondestructive testing, in particular to an adaptive ultrasonic image removal method based on SoS function modulation.

Background technique

Industrial ultrasonic testing is to use ultrasonic beams to scan the test piece, receive the echo signal of the target body, and use the ultrasonic imaging algorithm to visualize the defect echo information of the test piece to form an ultrasonic image. The position, size and shape of the defect can be directly displayed in the image, so that the inspector can observe the defect of the tested part from the image, and provide a basis for defect detection and evaluation. Artifacts in industrial ultrasonic images refer to non-target images formed in non-target areas, which are generated by principle errors and random errors. The existence of artifacts will affect the accurate judgment of actual defects.

There are three main reasons for artifacts. The first one is caused by the calculation algorithm of the spatial sound field during imaging, which belongs to the category of principle error generation. The artifacts caused by this reason are sometimes called the artifacts caused by isopathic lines. The second is caused by the phenomenon of ultrasonic echo signal smearing, which belongs to the category of random error generation. The aftershock of the received echo signal makes the echo duration longer. The amplitude of these smearing signals is not zero. When calculating the sound field, it is mistaken for a useful echo signal; the third is the artifact caused by random noise, which is randomly distributed, making the image blurred. Although many methods have been applied to the removal of artifacts in ultrasound images and achieved a certain effect of removing artifacts, they have not completely solved the artifacts caused by these three reasons. For example, by distinguishing the effective isopathic lines in the sound field calculation, the artifacts caused by the isopathic lines can be effectively removed; the smearing phenomenon of the echo signal can be weakened by using a sensor with large damping or a receiving circuit; the filtering method can Part of the echo noise is filtered out, but artifacts have always been a common problem in the field of ultrasound imaging, and there is no method that can effectively remove these artifacts.

Aiming at the problem of artifacts generated by ultrasonic images, the present invention proposes an adaptive ultrasonic image removal method based on SoS function modulation, using SoS function to modulate the ultrasonic echo signals collected by sensor detection and processing, and sampling at equal intervals to obtain To the value of the discrete signal sampling sequence, and then through the discrete Fourier transform of the discrete signal sampling sequence, the Fourier coefficient information of the original acoustic echo signal is obtained, and the signal parameter estimation is realized by using the spectrum estimation algorithm. The original signal is reconstructed, and finally it is acoustically imaged to obtain a defect ultrasonic image after removing artifacts.

Contents of the invention

In order to solve the problems existing in the prior art, the object of the present invention is to provide an adaptive ultrasonic image removal method based on SoS function modulation, which can correct the signal of the test piece defect detected by the ultrasonic sensor, through the imaging algorithm Ultrasound images with artifacts removed are obtained. On the basis of retaining the ultrasonic image defect information, the method has a remarkable artifact removal effect and can improve the accuracy and efficiency of defect assessment.

In order to achieve the above-mentioned purpose of the invention, the technical solution provided by the present invention is as follows: an adaptive ultrasonic image de-artifact method based on SoS function modulation, comprising the following steps:

1) Use the ultrasonic sensor to detect the test piece, receive the echo signal, and perform amplification, filtering and envelope processing on the echo signal to obtain the ultrasonic echo signal x(t) obtained by each scan;

2) Use the SoS function to perform amplitude modulation on the ultrasonic echo signal x(t) to obtain the transform domain signal y(t);

3) Sampling the transform domain signal y(t) at equal intervals to obtain a discrete sparse sampling sequence y[n] of the transform domain signal;

4) Discrete Fourier transform is performed on the discrete sparse sampling sequence y[n], and its Fourier coefficients contain the information of the ultrasonic echo signal;

5) Using the spectral estimation algorithm to estimate the parameters of the Fourier coefficients, obtain the time delay and amplitude parameters of the ultrasonic echo signal, and reconstruct the ultrasonic echo signal waveform in combination with the transmitted waveform of the ultrasonic sensor;

6) Acoustic imaging is performed using the reconstructed ultrasonic echo signal to obtain an ultrasonic pseudo-color image with artifacts removed.

Further, in the above step 1), the ultrasonic echo signal is generated by high-voltage pulse excitation piezoelectric wafer oscillation in the ultrasonic transducer, and the Gaussian model of the ultrasonic echo signal x(t) is represented as r(t), r( The mathematical model of t) is,

E表示激励脉冲的幅度；η为其脉宽因子；f₀为晶片的中心频率；K表示回波个数；φ_k为初相位；参数t_k和a_k携带待检测试块的缺陷回波信息，波达时刻t_k反映反射面的位置信息，回波幅值a_k反映超声波的声强损失情况。in,

E represents the amplitude of the excitation pulse; η is its pulse width factor; f ₀ is the center frequency of the chip; K represents the number of echoes; φ _k is the initial phase; parameters t _k and a _k carry the defect echo of the test block information, the moment of arrival t _k reflects the position information of the reflecting surface, and the echo amplitude a _k reflects the loss of ultrasonic sound intensity.

Further, the SoS function in the above step 2) is realized by the weighted sum of the sinc function, and its frequency domain expression is:

c_m≠0；Among them, τ is the duration of the signal; ω is the frequency of the signal; Ψ represents a continuous integer set, and the set length is determined by the information degree of freedom of the signal; c _m represents the weighting coefficient, which satisfies

c _m ≠ 0;

Converting the SoS function to the time domain, its expression is,

The waveform of s(t) is determined by {c _m } _m∈Ψ . Changing this series of parameters will affect the time-frequency domain response of the SoS function, and different modulation effects can be achieved.

Further, in the above step 2), the SoS function is used to perform amplitude modulation on the ultrasonic echo signal x(t) to obtain the modulated transform domain signal y(t),

where τ is the duration of the signal.

Further, in the above step 3), the transform domain signal y(t) is sampled at equal intervals, the sampling frequency is determined according to the innovation rate of the signal, and the sampling frequency is not less than the innovation rate of the signal, and the discrete sparse sampling sequence is obtained after sampling y[n];

The new interest rate is obtained through the following methods:

Assuming that the ultrasonic echo signal x(t) satisfies the condition that it can be represented by a finite number of information degrees of freedom, it can be expressed in a way based on information degrees of freedom, and its expression is,

为基函数，可选取为冲激函数或高斯函数；由基于有限信息率的信号x(t)的表达式可知，该信号仅由c_n,r和t_n确定。用连续函数C_x(t₁,t₂)来表示在时间间隔[t₁,t₂]内x(t)的信息自由度，则信号可以用有限个自由参量表示。以信号的局部时域区间来计算新息率，即有限信息自由度，并可通过时间窗对信号进行截断，从而得到信号的局部新息率P_τ(t)，In the formula, c _{n, r} and t _n represent the amplitude parameter and time delay parameter of the signal respectively,

is the basis function, which can be selected as an impulse function or a Gaussian function; from the expression of the signal x(t) based on the finite information rate, the signal is only determined by c _{n, r} and t _n . Using the continuous function C _x (t ₁ ,t ₂ ) to represent the information freedom of x(t) in the time interval [t ₁ ,t ₂ ], the signal can be represented by a finite number of free parameters. The innovation rate is calculated by the local time domain interval of the signal, that is, the limited degree of freedom of information, and the signal can be truncated through the time window, so as to obtain the local innovation rate P _τ (t) of the signal,

The local innovation rate represents the number of information degrees of freedom of the signal in the duration interval τ.

Furthermore, in the above step 4), the expression of x(t) based on the limited information degree of freedom is further simplified to form u(t) in the form of Dirac flow signal, expressed as,

t_k和a_k为超声回波信号x(t)高斯模型中的回波信号时延和幅值，也是后续需要估计的回波信号主要特征参数；in,

t _k and a _k are the time delay and amplitude of the echo signal in the Gaussian model of the ultrasonic echo signal x(t), and are also the main characteristic parameters of the echo signal that need to be estimated later;

The signal u(t) is expanded by Fourier series, and its Fourier coefficient U[m] can be expressed in the form of power series weighted sum, that is,

The Fourier coefficient U[m] includes time delay and amplitude information of the ultrasonic echo signal x(t), that is, Fourier coefficient information of the original ultrasonic echo signal.

Further, the above step 5) specifically includes the following steps:

5.1) Solve the weighted value and exponent of the power series through the spectral estimation algorithm;

5.2) Convolve the filter coefficient h _m with C[m], if the Z transformation of the zeroing filter satisfies H(β _k )=0, then their convolution result is zero, and the filter that meets the requirements can be obtained The coefficient h _m can be expressed as:

Let h ₀ =1, expand it to get:

h ₁ C[m-1]+h ₂ C[m-2]+…+h _K C[mK]＝-C[m]

The above formula contains K unknowns {h ₁ ,h ₂ ,…,h _K }, at least K equations are needed to solve it, that is, at least 2K continuous Fourier series coefficients are needed, and the equation system can have a unique solution.

5.3) Substitute the Fourier coefficient and the number of signal pulses into the equations in 5.2) to obtain the parameter information to be estimated

If a length is a continuous integer interval of A, Λ represents the value interval of the signal Fourier series coefficient, wherein, Λ=[-l,...,0,...,l], L=2l+1, then Fourier The relationship between the number of leaf coefficients L and the number of signal pulses K needs to satisfy:

L=2l+1≥2K

From the above derivation, it can be seen that only 2K continuous Fourier coefficients need to be obtained, and the above method can be used to estimate the amplitude and delay parameters of the signal

结合超声回波信号的高斯脉冲模型r(t)自适应重构出保留x(t)重要信息的超声回波信号。。5.4) According to the estimated parameters

Combined with the Gaussian pulse model r(t) of the ultrasonic echo signal, the ultrasonic echo signal retaining the important information of x(t) is adaptively reconstructed. .

Further, above-mentioned step 5.1) mid-spectrum estimation algorithm adopts zeroing filter method to solve algorithm, comprises the following steps:

的零化滤波器，令

其中m∈Z，零化滤波器的z变换为，5.1.1) The construction coefficient is

The nulling filter of

where m ∈ Z, the z-transform of the annihilating filter is,

令h₀＝1，经过因式分解后H(z)可进一步表示为，Let the zero point of H(z) be

Let h ₀ =1, after factorization H(z) can be further expressed as,

互异时，滤波器的零点就可以唯一表示信号的脉冲时延参数。When all delay parameters

When different, the zero point of the filter can uniquely represent the pulse delay parameter of the signal.

5.1.2) After finding the coefficient h _m of the nulling filter, the time delay parameter t _k can be further obtained by finding the root β _k of H(z).

5.1.3) Use U[m] to solve the amplitude parameter a _k .

Further, the signal reconstructed in the above step 6) is calculated for the sound field of the measured area, the sound field intensity of the spatial points is synthesized, and the magnitude of the synthesized sound field intensity is represented by the color scale, and then an ultrasonic pseudo-color image with artifacts removed is obtained.

The present invention has the following beneficial effects:

An adaptive ultrasonic image removal method based on SoS function modulation is invented, which can correct the signal data of the defect detected by the ultrasonic sensor, and obtain an ultrasonic image without artifacts through an imaging algorithm. The algorithm starts from the data, uses the SoS function to modulate the signal, obtains the Fourier coefficient information of the original ultrasonic echo signal through calculation processing, uses the spectrum estimation algorithm to realize the signal parameter estimation and reconstruction, and corrects the artifact data of the data, only retaining Effective information about defects is imaged. The effectiveness of the artifact removal algorithm is illustrated by comparing the reconstructed signal with the original ultrasonic echo signal in terms of amplitude, time delay and other information, as well as the final actual imaging effect. On the basis of retaining the ultrasonic image defect information, the algorithm has a remarkable artifact removal effect, which can improve the accuracy and efficiency of defect assessment.

Description of drawings

Fig. 1 is a flow chart of the inventive method;

Fig. 2 is the time domain and the frequency domain waveform figure of SoS function of the present invention; Wherein, (a) time domain; (b) frequency domain;

Fig. 3 is Embodiment 1 of the present invention; wherein, (a) schematic diagram of distribution of through-hole defects; (b) original signal and reconstructed signal of through-hole defects; (c) ultrasonic image of through-hole defects with artifacts; (d) Ultrasonic images of through-hole defects after removing artifacts by using the artifact removal method of the present invention;

Fig. 4 is Example 2 of the present invention; wherein, (a) schematic diagram of distribution of linear groove defects; (b) original signal and reconstructed signal of linear groove defects; (c) ultrasonic image of linear groove defects with artifacts; (d) The ultrasonic image of the linear groove defect after removing the artifacts by using the artifact removal method of the present invention.

Detailed ways

The specific forms of the present invention will be further described below in conjunction with the accompanying drawings and embodiments, and its flow chart is shown in FIG. 1 . It should be noted that the present invention can also be applied through other equivalent implementation manners, and the implementation manners described in the following examples illustrate the process and effect of the present invention by way of example.

As shown in Figure 1, the present invention is a kind of self-adaptive ultrasound image de-artifact method based on SoS function modulation, comprising the following steps:

1) Utilize the ultrasonic sensor to detect the tested piece, receive the echo signal, and amplify the echo signal, filter and get the envelope processing, and obtain the ultrasonic echo signal x(t) obtained by each scan; as the present invention In a preferred embodiment of the sensor, the sensor scans mechanically in a plane with equal step lengths during detection.

2) Use the SoS function to perform amplitude modulation on the ultrasonic echo signal x(t) to obtain the transform domain signal y(t);

3) Sampling the transform domain signal y(t) at equal intervals to obtain a discrete sparse sampling sequence y[n] of the transform domain signal;

4) Discrete Fourier transform is performed on the discrete sparse sampling sequence y[n], and its Fourier coefficients contain the information of the ultrasonic echo signal;

5) Using the spectral estimation algorithm to estimate the parameters of the Fourier coefficients, obtain the time delay and amplitude parameters of the ultrasonic echo signal, and reconstruct the ultrasonic echo signal waveform in combination with the transmitted waveform of the ultrasonic sensor;

6) Acoustic imaging is performed using the reconstructed ultrasonic echo signal to obtain an ultrasonic pseudo-color image with artifacts removed.

As a preferred embodiment of the present invention, in the above step 1), the ultrasonic echo signal is generated by high-voltage pulse excitation piezoelectric wafer oscillation in the ultrasonic transducer, and the Gaussian model of the ultrasonic echo signal x(t) is represented as r( t), the mathematical model of r(t) is,

E表示激励脉冲的幅度；η为其脉宽因子；f₀为晶片的中心频率；K表示回波个数；φ_k为初相位；参数t_k和a_k携带待检测试块的缺陷回波信息，波达时刻t_k反映反射面的位置信息，回波幅值a_k反映超声波的声强损失情况。in,

E represents the amplitude of the excitation pulse; η is its pulse width factor; f ₀ is the center frequency of the chip; K represents the number of echoes; φ _k is the initial phase; parameters t _k and a _k carry the defect echo of the test block information, the moment of arrival t _k reflects the position information of the reflecting surface, and the echo amplitude a _k reflects the loss of ultrasonic sound intensity.

As a preferred embodiment of the present invention, above-mentioned step 2) in the SoS function is realized by sinc function weighted sum, and its frequency domain expression is:

c_m≠0；Among them, τ is the duration of the signal; ω is the frequency of the signal; Ψ represents a continuous integer set, and the set length is determined by the information degree of freedom of the signal; c _m represents the weighting coefficient, which satisfies

c _m ≠ 0;

Converting the SoS function to the time domain, its expression is,

The waveform of s(t) is determined by {c _m } _m∈Ψ . Changing this series of parameters will affect the time-frequency domain response of the SoS function, and different modulation effects can be achieved. The time-domain and frequency-domain waveform diagrams of the SoS function are shown in Figure 2(a) and (b), respectively, where the frequency

As a preferred embodiment of the present invention, in the above step 2), the SoS function is used to perform amplitude modulation on the ultrasonic echo signal x(t) to obtain the modulated transform domain signal y(t),

where τ is the duration of the signal.

As a preferred embodiment of the present invention, in the above step 3), the transform domain signal y(t) is sampled at equal intervals, the sampling frequency is determined according to the innovation rate of the signal, and the sampling frequency is not less than the innovation rate of the signal. After sampling Obtain discrete sparse sampling sequence y[n];

The new interest rate is obtained through the following methods:

Assuming that the ultrasonic echo signal x(t) satisfies the condition that it can be represented by a finite number of information degrees of freedom, it can be expressed in a way based on information degrees of freedom, and its expression is,

为基函数，可选取为冲激函数或高斯函数；由基于有限信息率的信号x(t)的表达式可知，该信号仅由c_n,r和t_n确定。用连续函数C_x(t₁,t₂)来表示在时间间隔[t₁,t₂]内x(t)的信息自由度，则信号可以用有限个自由参量表示。以信号的局部时域区间来计算新息率，即有限信息自由度，并可通过时间窗对信号进行截断，从而得到信号的局部新息率P_τ(t)，In the formula, c _{n, r} and t _n represent the amplitude parameter and time delay parameter of the signal respectively,

is the basis function, which can be selected as an impulse function or a Gaussian function; from the expression of the signal x(t) based on the finite information rate, the signal is only determined by c _{n, r} and t _n . Using the continuous function C _x (t ₁ ,t ₂ ) to represent the information freedom of x(t) in the time interval [t ₁ ,t ₂ ], the signal can be represented by a finite number of free parameters. The innovation rate is calculated by the local time domain interval of the signal, that is, the limited degree of freedom of information, and the signal can be truncated through the time window, so as to obtain the local innovation rate P _τ (t) of the signal,

The local innovation rate represents the number of information degrees of freedom of the signal in the duration interval τ.

As a preferred embodiment of the present invention, in the above step 4), the expression of x(t) based on the limited degree of freedom of information is further simplified to form a form u(t) represented by a Dirac flow signal, expressed as,

t_k和a_k为超声回波信号x(t)高斯模型中的回波信号时延和幅值，也是后续需要估计的回波信号主要特征参数；in,

t _k and a _k are the time delay and amplitude of the echo signal in the Gaussian model of the ultrasonic echo signal x(t), and are also the main characteristic parameters of the echo signal that need to be estimated later;

The signal u(t) is expanded by Fourier series, and its Fourier coefficient U[m] can be expressed in the form of power series weighted sum, that is,

The Fourier coefficient U[m] includes time delay and amplitude information of the ultrasonic echo signal x(t), that is, Fourier coefficient information of the original ultrasonic echo signal.

As a preferred embodiment of the present invention, the above step 5) specifically includes the following steps:

5.1) Solve the weighted value and exponent of the power series through the spectral estimation algorithm;

5.2) Convolve the filter coefficient h _m with C[m], if the Z transformation of the zeroing filter satisfies H(β _k )=0, then their convolution result is zero, and the filter that meets the requirements can be obtained The coefficient h _m can be expressed as:

Let h ₀ =1, expand it to get:

h ₁ C[m-1]+h ₂ C[m-2]+…+h _K C[mK]＝-C[m]

The above formula contains K unknowns {h ₁ ,h ₂ ,…,h _K }, at least K equations are needed to solve it, that is, at least 2K continuous Fourier series coefficients are needed, and the equation system can have a unique solution.

5.3) Substitute the Fourier coefficient and the number of signal pulses into the equations in 5.2) to obtain the parameter information to be estimated

If a length is a continuous integer interval of A, Λ represents the value interval of the signal Fourier series coefficient, wherein, Λ=[-l,...,0,...,l], L=2l+1, then Fourier The relationship between the number of leaf coefficients L and the number of signal pulses K needs to satisfy:

L=2l+1≥2K

From the above derivation, it can be seen that only 2K continuous Fourier coefficients need to be obtained, and the above method can be used to estimate the amplitude and delay parameters of the signal

结合超声回波信号的高斯脉冲模型r(t)自适应重构出保留x(t)重要信息的超声回波信号。5.4) According to the estimated parameters

Combined with the Gaussian pulse model r(t) of the ultrasonic echo signal, the ultrasonic echo signal retaining the important information of x(t) is adaptively reconstructed.

As a preferred embodiment of the present invention, above-mentioned steps 5.1) spectrum estimation algorithm adopts zeroing filter method to solve algorithm, comprises the following steps:

的零化滤波器，令

其中m∈Z，零化滤波器的z变换为，5.1.1) The construction coefficient is

The nulling filter of

where m ∈ Z, the z-transform of the annihilating filter is,

令h₀＝1，经过因式分解后H(z)可进一步表示为，Let the zero point of H(z) be

Let h ₀ =1, after factorization H(z) can be further expressed as,

互异时，滤波器的零点就可以唯一表示信号的脉冲时延参数。When all delay parameters

When different, the zero point of the filter can uniquely represent the pulse delay parameter of the signal.

5.1.2) After finding the coefficient h _m of the nulling filter, the time delay parameter t _k can be further obtained by finding the root β _k of H(z).

5.1.3) Use U[m] to solve the amplitude parameter a _k .

As a preferred embodiment of the present invention, the signal reconstructed in the above step 6) is used to calculate the sound field of the measured area, synthesize the sound field strength of the spatial point, and express the magnitude of the synthesized sound field strength with a color scale, and then obtain the ultrasonic artifact with the artifact removed. color image.

The technical solutions of the present invention will be further described below in conjunction with specific embodiments.

Specific embodiment 1

(1) Select the through-hole standard defect test block for the test. The geometric distribution of the test block and defects is shown in Figure 3(a). The diameter of the through-hole defect is set to 2mm. The center frequency is 5MHZ, the diameter of the probe is 13.0mm, the model is I2-1P25F70-H (IGI1320), and the focal length is 78mm (the focal length in water). The test block is made of aluminum, and its ultrasonic sound velocity is 6300m/s. The vertical incidence detection method is adopted, the couplant is water, the wave velocity in the couplant is 1480m/s, the focus is set at the center of the test block, that is, the distance from the focal point to the surface of the workpiece is 17mm, and the thickness of the water layer is 20mm. During the detection, the sensor adopts the self-sending, self-receiving and self-receiving mode, the pulse width is 200ns, and the voltage is 200V. The mechanical structure controls the probe to move in equal steps of 0.1mm, and the moving speed is 10mm/s. The upper left corner of the test block is Starting from the origin, the detection is carried out along the X-axis in the XOY plane, so that the scanning range of the sensor covers the entire block to be tested. The echo signal received by the sensor is amplified (the gain is 25dB), filtered (2-6MHz band-pass filter) and enveloped. The echo is received and the ultrasonic echo signal obtained by each detection is obtained, and the sampling frequency is 80MHz. Because the ultrasonic signal is constantly attenuating, the data of the first surface wave and the first bottom wave are selected for processing and imaging.

(2) Realize the SoS function through the weighted sum of the sinc function, and the weighting coefficients c _m all take 1, then the time-domain and frequency-domain waveforms of the SoS function are shown in Fig. 2 . The collected ultrasonic echo signal is modulated with SoS function, and the signal is transformed in time domain so that the transformation domain information required for parameter estimation can be obtained from the down-sampled value in the subsequent reconstruction process.

(3) Sampling the modulated signal at an equal interval of 0.067us, requiring that the sampling frequency should not be less than the innovation rate of the signal, and obtain the discrete signal sampling sequence value;

(4) Obtain the Fourier coefficient information of the original ultrasonic echo sweep signal by computing the discrete Fourier transform of the discrete signal sampling sequence;

(5) Using the spectral estimation algorithm to estimate the signal amplitude and time delay parameters, and combine the known waveform with Gaussian characteristics to restore the waveform and reconstruct the signal. The reconstruction accuracy is 13.5, and the echo number of the signal is 2, that is, the number of signal pulses. The waveforms of the primary surface wave and the defect are reconstructed by using the amplitude delay of the surface wave and the defect, and the reconstructed signal and the original are shown in Fig. 3(b).

(6) Using the same ultrasonic imaging algorithm to perform imaging on the ultrasonic echo signal before being processed by the ultrasonic image removal method proposed by the present invention and the reconstructed signal. Figure 3(c) shows the B-scan image of ultrasonic pseudo-color with artifacts, and Figure 3(d) shows the B-scan image of ultrasonic false-color without artifacts. Taking the image with artifacts as the standard, the peak signal-to-noise ratio and structural similarity of the removed artifacts and standard images are calculated, which are 24.306dB and 0.931 respectively, and the images are not distorted after removing artifacts. It can be seen that the method for removing artifacts in ultrasonic images proposed by the present invention can effectively remove artifacts in ultrasonic images of through-hole standard defect test blocks, especially near-surface artifacts.

Specific embodiment 2

The groove-type standard defect test block is selected for the test, and the geometric distribution of the test block and defects is shown in Figure 4(a). The size of the linear groove is set to be 5×3×2mm ³ , the test block is made of aluminum material, and its ultrasonic sound velocity is 6300m/s. The ultrasonic sensor, detection method and imaging method used are the same as those in Embodiment 1. When using an adaptive ultrasonic image de-artifact method based on SoS function modulation proposed by the present invention, the time interval is 0.067us, the echo number K=2 of the signal, the original signal and the reconstructed signal are shown in Figure 4 (b) . Figure 4(c) shows the ultrasound pseudo-color B-scan image without artifact removal, and Figure 4(d) shows the ultrasound pseudo-color B-scan image with artifact removal. The peak signal-to-noise ratio and structural similarity of the artifact-removed and standard images were 23.213dB and 0.932, respectively, and the image was not distorted after removing the artifacts. It can be seen that the method for removing artifacts in ultrasonic images proposed by the present invention can effectively remove artifacts in ultrasonic images of groove-type standard defect test blocks.

The above embodiments are only used to illustrate the technical solution of the present invention, not to limit it. For those skilled in the relevant technical fields, equivalent implementations and improvements without departing from the principles and technical spirit of the present invention are also within the protection scope of the present invention.

Claims (9)

1. an adaptive ultrasonic image de-artifact method based on SoS function modulation, is characterized in that, comprises the steps: 1) Use the ultrasonic sensor to detect the test piece, receive the echo signal, and perform amplification, filtering and envelope processing on the echo signal to obtain the ultrasonic echo signal x(t) obtained by each scan; 2) Use the SoS function to perform amplitude modulation on the ultrasonic echo signal x(t) to obtain the transform domain signal y(t); 3) Sampling the transform domain signal y(t) at equal intervals to obtain a discrete sparse sampling sequence y[n] of the transform domain signal; 4) Discrete Fourier transform is performed on the discrete sparse sampling sequence y[n], and its Fourier coefficients contain the information of the ultrasonic echo signal; 5) Using the spectral estimation algorithm to estimate the parameters of the Fourier coefficients, obtain the time delay and amplitude parameters of the ultrasonic echo signal, and reconstruct the ultrasonic echo signal waveform in combination with the transmitted waveform of the ultrasonic sensor; 6) Acoustic imaging is performed using the reconstructed ultrasonic echo signal to obtain an ultrasonic pseudo-color image with artifacts removed. 2. the adaptive ultrasonic image de-artifact method based on SoS function modulation as claimed in claim 1, is characterized in that, in described step 1), ultrasonic echo signal excites the piezoelectric in the ultrasonic transducer by high-voltage pulse Chip oscillation is generated, and the Gaussian model of the ultrasonic echo signal x(t) is represented as r(t), and the mathematical model of r(t) is,

in,

E represents the amplitude of the excitation pulse; η is its pulse width factor; f ₀ is the center frequency of the chip; K represents the number of echoes; φ _k is the initial phase; parameters t _k and a _k carry the defect echo of the test block information, the moment of arrival t _k reflects the position information of the reflecting surface, and the echo amplitude a _k reflects the loss of ultrasonic sound intensity. 3. the adaptive ultrasonic image removal artifact method based on SoS function modulation as claimed in claim 1, is characterized in that, in described step 2), SoS function is realized by sinc function weighted sum, and its frequency domain expression is:

Among them, τ is the duration of the signal; ω is the frequency of the signal; Ψ represents a continuous integer set, and the set length is determined by the information degree of freedom of the signal; c _m represents the weighting coefficient, which satisfies

c _m ≠ 0; Converting the SoS function to the time domain, its expression is,

The waveform of s(t) is determined by {c _m } _m∈Ψ , different parameter values can achieve different modulation effects. 4. the adaptive ultrasonic image de-artifact method based on SoS function modulation as claimed in claim 1, is characterized in that in described step 2), utilizes SoS function to carry out amplitude modulation to ultrasonic echo signal x (t) and obtains The modulated transform domain signal y(t),

where τ is the duration of the signal. 5. The adaptive ultrasonic image de-artifacting method based on SoS function modulation as claimed in claim 1, is characterized in that, in described step 3) carries out equidistant sampling to transform domain signal y (t), sampling frequency according to signal The innovation rate of the signal is determined, the sampling frequency is not less than the innovation rate of the signal, and the discrete sparse sampling sequence y[n] is obtained after sampling; The new interest rate is obtained through the following methods: Assuming that the ultrasonic echo signal x(t) satisfies the condition that it can be represented by a finite number of information degrees of freedom, it can be expressed in a manner based on information degrees of freedom, and its expression is,

In the formula, c _{n, r} and t _n represent the amplitude parameter and time delay parameter of the signal respectively,

As the basis function, the continuous function C _x (t ₁ ,t ₂ ) is used to represent the information freedom of x(t) in the time interval [t ₁ ,t ₂ ], and the innovation rate is calculated by the local time domain interval of the signal , that is, limited information degrees of freedom, and the signal can be truncated through the time window, so as to obtain the local innovation rate P _τ (t) of the signal,

The local innovation rate represents the number of information degrees of freedom of the signal in the duration interval τ. 6. the adaptive ultrasound image de-artifacting method based on SoS function modulation as claimed in claim 1, is characterized in that, in described step 4), the expression based on limited information degree of freedom of x (t) is expressed as Dirac The stream signal u(t), denoted as,

in,

t _k ∈ [0,τ), a _k ∈ R, t _k and a _k are the echo signal delay and amplitude in the Gaussian model of the ultrasonic echo signal x(t); The signal u(t) is expanded by Fourier series, and its Fourier coefficient U[m] can be expressed in the form of power series weighted sum, that is,

The Fourier coefficient U[m] includes time delay and amplitude information of the ultrasonic echo signal x(t), that is, Fourier coefficient information of the original ultrasonic echo signal. 7. the adaptive ultrasound image de-artifact method based on SoS function modulation as claimed in claim 1, is characterized in that, described step 5) specifically comprises the following steps: 5.1) Solve the weighted value and exponent of the power series through the spectral estimation algorithm; 5.2) Convolve the filter coefficient h _m with C[m], if the Z transformation of the zeroing filter satisfies H(β _k )=0, then their convolution result is zero, and the filter that meets the requirements can be obtained Coefficient h _m , expressed as:

Let h ₀ =1, expand it to get: h ₁ C[m-1]+h ₂ C[m-2]+…+h _K C[mK]＝-C[m] In the above formula, at least 2K continuous Fourier series coefficients are required for the equation system to have a unique solution; 5.3) Substitute the Fourier coefficient and the number of signal pulses into the equations in 5.2) to obtain the parameter information to be estimated

If a length is a continuous integer interval of A, Λ represents the value interval of the signal Fourier series coefficient, wherein, Λ=[-l,...,0,...,l], L=2l+1, then Fourier The relationship between the number of leaf coefficients L and the number of signal pulses K needs to satisfy: L=2l+1≥2K Obtain 2K continuous Fourier coefficients to estimate the amplitude and delay parameters of the signal

5.4) According to the estimated parameters

Combined with the Gaussian pulse model r(t) of the ultrasonic echo signal, the ultrasonic echo signal retaining the important information of x(t) is adaptively reconstructed. 8. the adaptive ultrasound image de-artifact method based on SoS function modulation as claimed in claim 7, is characterized in that, described step 5.1) spectrum estimation algorithm adopts zeroing filter method to solve algorithm, comprises the following steps: 5.1.1) The construction coefficient is

The nulling filter of

where m ∈ Z, the z-transform of the annihilating filter is,

Let the zero point of H(z) be

Let h ₀ =1, after factorization H(z) can be expressed as,

When all delay parameters

When different, the zero point of the filter can uniquely represent the pulse delay parameter of the signal; 5.1.2) Find the zeroing filter coefficient h _m , and obtain the time delay parameter t _k by finding the root β _k of H(z); 5.1.3) Use U[m] to solve the amplitude parameter a _k . 9. The adaptive ultrasonic image de-artifacting method based on SoS function modulation as claimed in claim 1, characterized in that, the signal reconstructed in the step 6) carries out the sound field calculation of the measured area, and the spatial point sound field intensity Synthesize, and express the intensity of the synthesized sound field in color scale, and then obtain the ultrasonic pseudo-color image with artifacts removed.

Images