CN110728646A - Time domain segmented imaging method based on terahertz effective signal extraction - Google Patents
Time domain segmented imaging method based on terahertz effective signal extraction Download PDFInfo
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Abstract
A time domain segmented imaging method based on terahertz effective signal extraction is disclosed, which comprises the following steps: (1) terahertz effective signal extraction and time correction, and (2) time-segmented imaging based on effective signal extraction. The method can effectively improve the quality of the terahertz image, improve the signal-to-noise ratio and has a remarkable effect on the multi-depth defect detection of the composite material.
Description
Technical Field
The invention relates to a terahertz imaging technology, in particular to a terahertz effective signal extraction and time domain imaging method.
Background
Terahertz is an electromagnetic wave with the frequency range of 0.1-10 THz, has the characteristics of nonionization, high penetration, strong coherence, high resolution and the like, can penetrate through almost all insulating materials such as paper, coatings, foams, plastics, glass and the like, and is widely applied to the aspects of biomedical detection, nondestructive inspection, communication, radar and safety inspection. In recent ten years, the rapid development of the terahertz technology in the field of nondestructive testing is promoted by the progress of the terahertz spectral imaging technology, and particularly, the terahertz spectral imaging technology has excellent performance in the aspect of nondestructive testing of composite materials. However, in actual composite material terahertz defect imaging, due to the influences of factors such as power limitation of a terahertz source, high environmental noise, compact and non-penetrable composite material and the like, background gray scale of a terahertz image is not uniformly distributed, edge resolution is poor, image quality is not high, and detection of defects in a composite material is seriously influenced. Theoretically, the most effective method for improving the quality of the terahertz image is realized by improving hardware conditions, as long as the aperture of the terahertz beam is small enough, the resolution of the image is completely dependent on the scanning step of the raster scanning platform, and the smaller the scanning step is, the higher the resolution of the terahertz image is. However, the terahertz source is limited at present, and if the aperture of the terahertz beam is too small, the output terahertz power is weaker, which is still not favorable for imaging. Therefore, it takes a lot of money and time to improve the image quality by improving the performance of the hardware such as the radiation source, the optical device, the detector, etc., and has limitations. In contrast, the quality of the terahertz image can be effectively improved through the research of the imaging method and the later-stage image processing algorithm, and the cost is low, so that the research on the terahertz imaging method has very important research significance and engineering application value for improving the quality of the terahertz image and promoting the development of the terahertz technology in the field of nondestructive testing.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a time domain segmented imaging method based on terahertz effective signal extraction, which comprises the following steps:
(1) terahertz effective signal extraction and time correction
Before imaging, the influence of surface reflection signals and signal sequence delay needs to be eliminated, and effective signals are extracted and time-corrected; the specific implementation steps are as follows:
step 1: selecting valid signal range
The starting point of the effective signal is t-Time from the first peak value after the maximum peak value of the Time domain signal1I.e. the first peak after the surface reflection signal, Time-t1Taking up, estimating the end point of the signal as t according to the known thickness of the sample2＝t1+ d/v, wherein d is the thickness of the sample piece, and v is the wave velocity of the terahertz wave; so that the range of the desired valid signal is from t1Time t2Time of day;
step 2: time correction
Will t1Setting the time as an initial time, namely 0 time, namely finishing the signal delay correction;
(2) time segmented imaging based on effective signal extraction
Step 1: time segmentation
Dividing the terahertz detection effective signal sequence obtained at different positions into N segments on average in time, namely A1To ANThe sections correspond to different depth ranges of the sample piece, so that defect signals are relatively obvious in each section;
step 2: segmented imaging
Respectively selecting characteristic parameters related to the effective time signal sequence in each time period for imaging, namely converting the characteristic parameters of each pixel point into corresponding gray values to obtain high-quality planar imaging results of N different depth ranges; multiple plane images of the same region obtained by different time domain segmentation have information redundancy and complementarity, so that a terahertz detection plane image containing all defect information needs to be synthesized by a subsequent image fusion method;
and step 3: image fusion
Adopting an image fusion method based on wavelet decomposition, wherein a weighted average method is adopted as a fusion rule; the fusion rule is expressed as:
wherein, CFFor fusing result coefficient matrices, CiWavelet decomposition coefficient matrix, m, for the ith imageiAnd obtaining an imaging result after image fusion for the weighting coefficient of the ith image.
The method can effectively improve the quality of the terahertz image, improve the signal-to-noise ratio and have a remarkable effect on the multi-depth defect detection of the composite material.
Drawings
Fig. 1 shows a typical terahertz time-domain detection signal;
fig. 2 shows a terahertz effective signal extraction and time delay correction result;
fig. 3 shows a terahertz image of a conventional imaging method, wherein fig. 3(a), (b), (c) respectively show terahertz peak-to-peak imaging, maximum value imaging and maximum time imaging;
fig. 4 shows time-sliced signal peak-to-peak imaging results, in which a1, a2, A3, a4 sequentially show imaging results of four time slices, respectively;
FIG. 5 illustrates the principle of a time segmented imaging method based on active signal extraction;
FIG. 6 shows terahertz images imaged by the method of the present invention, wherein FIG. 6(a), (b), (c) show the image fusion results with weight coefficients of (1, 1, 1, 1; 1), (1, 1, 1.5), (1, 1, 1, 2), respectively.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The invention relates to a time domain segmented imaging method based on terahertz effective signal extraction, which comprises the following steps:
(1) terahertz effective signal extraction and time correction
The pulse terahertz detection signal is generally shown in fig. 1, and it can be seen from the figure that the amplitude of the initial reflected wave signal is much larger than that of other signals, and the initial reflected echo has no effect in defect detection imaging. However, when the traditional imaging based on the characteristics of signal peak-to-peak value and the like is used, the signal plays a main role, and a useful defect signal is submerged, so that the defect imaging is greatly influenced. Meanwhile, due to factors such as unevenness of the surface of the sample, time delay of a signal sequence is caused, and the imaging effect is influenced. Therefore, before imaging, the influence of surface reflection signals and signal sequence delay needs to be eliminated, and effective signals need to be extracted and time-corrected. The specific implementation steps are as follows:
step 1: selecting valid signal range
The starting point of the effective signal is at the first peak after the maximum peak of the Time domain signal (Time ═ t)1) I.e. at the first peak after the surface reflection signal (Time t)1) The end point of the signal is taken up and estimated as t from the (known) thickness of the sample2＝t1+ d/v, wherein d is the thickness of the sample piece, and v is the wave velocity of the terahertz wave. So that the range of the desired valid signal is from t1Time t2The time of day.
Step 2: time correction
Will t1The time is set as the initial time (time 0), i.e., the signal delay correction is completed.
Fig. 2 is a result of extracting an effective signal portion containing defect information in the signal of fig. 1, and it can be seen that after the effective signal is extracted and time delay corrected, since there is no interference from surface reflection and other strong signals, the defect signals (at the dashed oval frame) at different positions of the effective signal segment are more obvious than the original signal.
(2) Time segmented imaging based on effective signal extraction
In the existing terahertz detection imaging method, imaging by using amplitude values or time information of time domain signals and the like is the most common imaging method. As shown in fig. 3, terahertz images of various conventional time-domain signal imaging methods are shown. According to the traditional terahertz detection time-domain imaging method, a characteristic value is selected for imaging a whole time-domain signal by once pulling through, but according to the distribution characteristic of the terahertz detection effective signal shown in fig. 2, the noise and the defect signal of the terahertz detection signal have the characteristic that the noise and the defect signal weaken along with the time. Therefore, the amplitude of the defect signal at a deeper position in the sample is smaller than that of the noise signal at the initial stage, and the information of the defect at the deeper position is easily lost by adopting the traditional imaging method. In addition, only the defect with the shallowest depth can be displayed under the condition that the overlapped defects exist at different depths at the same position, so that the problem that the defect in a deeper position is lost or the imaging quality is poor in a plane imaging result is caused.
Aiming at the problem, the invention provides a time segmentation imaging method based on effective signal extraction, and the specific implementation scheme is as follows:
step 1: time segmentation
Dividing the terahertz detection effective signal sequence obtained at different positions into N sections (A) on average in time1To AN) And corresponding to the range of different depths of the sample, the defect signals are relatively obvious in each section.
Step 2: segmented imaging
The characteristic parameters related to the effective time signal sequence in each time period are respectively selected for imaging, that is, the characteristic parameters of each pixel point are converted into corresponding gray values (the conversion method is well known to those skilled in the art), such as a signal peak value, a maximum value and the like, so that high-quality planar imaging results of N different depth ranges can be obtained, and as shown in fig. 4, the time-segmented signal peak-value imaging results are obtained. Multiple plane images of the same region obtained by different time domain segmentation have information redundancy and complementarity, so that a terahertz detection plane image containing all defect information needs to be synthesized by an image fusion technology.
And step 3: image fusion
The image fusion method is multiple, the image fusion method based on wavelet decomposition is adopted in the invention, and the fusion rule adopts a weighted average method. The fusion rule can be expressed as:
wherein, CFFor fusing result coefficient matrices, CiRespectively is the wavelet decomposition coefficient matrix of the ith image (the coefficient matrix can be obtained by utilizing the wavelet decomposition function in matlab software), and miIs the weighting coefficient (artificially given) of the ith image. FIG. 6 shows the result of the image fusion of the present invention. By comparing with fig. 3, it can be found that the image quality is significantly improved and the defect outline is more clear and obvious.
According to the method, after the terahertz effective signal is extracted, the influence of the reflection echo on the imaging of the surface of the sample piece is effectively reduced, time periods are divided according to the thickness of the sample piece for imaging respectively, and finally, the terahertz imaging result is obtained by using an image fusion technology. Compared with the traditional terahertz imaging method, the method provided by the invention can obviously improve the condition that the imaging effect at the depth of the sample is not good due to the attenuation of the terahertz signal, can more easily find the defect at the depth of the sample by combining the noise reduction treatment, and has important significance for the development of the terahertz nondestructive testing technology.
Claims (1)
1. The time domain segmented imaging method based on terahertz effective signal extraction is characterized by comprising the following steps:
(1) terahertz effective signal extraction and time correction
Before imaging, the influence of surface reflection signals and signal sequence delay needs to be eliminated, and effective signals are extracted and time-corrected; the specific implementation steps are as follows:
step 1: selecting valid signal range
The starting point of the effective signal is t-Time from the first peak value after the maximum peak value of the Time domain signal1I.e. the first peak after the surface reflection signal, Time-t1Taking up, estimating the end point of the signal as t according to the known thickness of the sample2＝t1+ d/v, wherein d is the thickness of the sample piece, and v is the wave velocity of the terahertz wave; so that the range of the desired valid signal is from t1Time t2Time of day;
step 2: time correction
Will t1Setting the time as an initial time, namely 0 time, namely finishing the signal delay correction;
(2) time segmented imaging based on effective signal extraction
Step 1: time segmentation
Dividing the terahertz detection effective signal sequence obtained at different positions into N segments on average in time, namely A1To ANThe sections correspond to different depth ranges of the sample piece, so that defect signals are relatively obvious in each section;
step 2: segmented imaging
Respectively selecting characteristic parameters related to the effective time signal sequence in each time period for imaging, namely converting the characteristic parameters of each pixel point into corresponding gray values to obtain high-quality planar imaging results of N different depth ranges; multiple plane images of the same region obtained by different time domain segmentation have information redundancy and complementarity, so that a terahertz detection plane image containing all defect information needs to be synthesized by a subsequent image fusion method;
and step 3: image fusion
Adopting an image fusion method based on wavelet decomposition, wherein a weighted average method is adopted as a fusion rule; the fusion rule is expressed as:
wherein, CFFor fusing result coefficient matrices, CiWavelet decomposition coefficient matrix, m, for the ith imageiAnd obtaining an imaging result after image fusion for the weighting coefficient of the ith image.
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| CN112787203A (en) * | 2020-12-17 | 2021-05-11 | 中国人民解放军空军工程大学 | Transmission type silicon-based electrically-adjustable terahertz dynamic device and preparation method thereof |
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| CN104764713A (en) * | 2015-04-06 | 2015-07-08 | 何赟泽 | Terahertz transient thermal imaging detection and tomographic imaging system and method |
| CN107356599A (en) * | 2017-06-23 | 2017-11-17 | 厦门大学 | A kind of Terahertz lossless detection method of ceramic matric composite |
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| CN104764713A (en) * | 2015-04-06 | 2015-07-08 | 何赟泽 | Terahertz transient thermal imaging detection and tomographic imaging system and method |
| CN107356599A (en) * | 2017-06-23 | 2017-11-17 | 厦门大学 | A kind of Terahertz lossless detection method of ceramic matric composite |
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| 张瑾等: "小波图像融合在太赫兹无损检测中的应用", 《光谱学与光谱分析》 * |
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| CN112787203A (en) * | 2020-12-17 | 2021-05-11 | 中国人民解放军空军工程大学 | Transmission type silicon-based electrically-adjustable terahertz dynamic device and preparation method thereof |
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