Ultrasonic Testing Signal Denoising Based on Matching Pursuits

2013 ◽  
Vol 397-400 ◽  
pp. 2215-2218
Author(s):  
Xiu Li Du ◽  
Ying Hua Jiang
Keyword(s):  
White Noise ◽  
Ultrasonic Testing ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Signal Denoising ◽  
Signal To Noise ◽  
Matching Pursuits ◽  
Testing Signal ◽  
Mp Method ◽  
The Relationship

When matching pursuits (MP) method was used for noise suppression of ultrasonic testing signals, the number of matched atoms affects the denoising performance. The relationship between the number of the matched atoms and denoising capability was analyzed, using the root mean square error (RMSE) and improvement of signal-to-noise ratio (SNR) to evaluate denoising performance. The simulated signals with white noise at different SNR and experimental signal with white noise and grain noise were analyzed respectively, and the results show that the MP method can remove the white noise and grain noise effectively. Moreover the best denoising performance can be arrived if the number of matched atoms is appropriate. At last, the selection principle of atoms number is given.

Instantaneous Frequency Estimation of Ultrasonic Testing Signal Based on Matching Pursuits

2013 ◽  
Vol 631-632 ◽  
pp. 1367-1372 ◽  
Author(s):  
Xiu Li Du
Keyword(s):  
Instantaneous Frequency ◽  
Ultrasonic Testing ◽  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Frequency Representation ◽  
Matching Pursuits ◽  
Testing Signal ◽  
Suppress Noise

The differences of instantaneous frequency (IF) characteristics between the defect echo and the noise can be used to detect defect and suppress noise for ultrasonic testing signal. Therefore, the IF is one of the important instantaneous parameters of ultrasonic testing signal. To estimate the IF of ultrasonic testing signals more effectively, the peak of time-frequency representation (TFR) from matching pursuits (MP) decomposition is proposed. The performances of IF estimators are compared on the simulated signals at different signal-to-noise ratio (SNR) and the real ultrasonic testing signal. The simulation results present that the proposed method can estimate accurate IF at different SNR.

Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method

2020 ◽  
Vol 28 (6) ◽  
pp. 1037-1054
Author(s):  
Temitope E. Komolafe ◽  
Qiang Du ◽  
Yin Zhang ◽  
Zhongyi Wu ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Breast Tissue ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Optimization Method ◽  
Dual Energy ◽  
Hybrid Optimization ◽  
Signal To Noise ◽  
Breast Ct ◽  
Different Levels

BACKGROUND: Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection. OBJECTIVE: To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast. METHODS: The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method. RESULTS: The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem. CONCLUSIONS: This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

Improving signal-to-noise ratio in ultrasonic testing

2016 ◽  
Vol 52 (6) ◽  
pp. 310-314
Author(s):  
I. A. Krivosheev ◽  
M. I. Ignat’eva ◽  
A. I. Shamurina
Keyword(s):  
Ultrasonic Testing ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

Effect of White Noise and Dichotomous Noise in a Bistable System

2011 ◽  
Vol 295-297 ◽  
pp. 2143-2146 ◽  
Author(s):  
Feng Guo ◽  
Xiao Feng Cheng ◽  
Xiao Dong Yuan ◽  
Shao Bo He
Keyword(s):  
White Noise ◽  
Adiabatic Approximation ◽  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Bistable System ◽  
Signal To Noise ◽  
Dichotomous Noise ◽  
System Parameters ◽  
Additive White Noise ◽  
Asymmetric Dichotomous Noise

The stochastic resonance in a bistable system subject to asymmetric dichotomous noise and multiplicative and additive white noise is investigated. By using the properties of the dichotomous noise, under the adiabatic approximation condition, the expression of the signal-to-noise ratio (SNR) is obtained. It is found that the SNR is a non-monotonic function of the asymmetry of the dichotomous noise, and it varies non-monotonously with the intensities of the multiplicative and additive noise as well as with the system parameters. Moreover, the SNR depends on the correlation rate of the dichotomous noise.

Application of Phase-Sensitivity Detector in Air-Coupled Ultrasonic Testing

2012 ◽  
Vol 170-173 ◽  
pp. 3125-3129
Author(s):  
Wei Dong ◽  
Zi Wei Zhou ◽  
Zheng Gan Zhou
Keyword(s):  
Ultrasonic Testing ◽  
Testing Effect ◽  
Signal To Noise Ratio ◽  
Research Result ◽  
Phase Sensitivity ◽  
Signal To Noise ◽  
Detection Technology ◽  
Long Time ◽  
Phase Signal ◽  
Sensitivity Detection

The signal to noise ration of air-coupled ultrasonic testing is very poor and there is long time pulse residue, so it need adopt appropriate signal processing method to enhancing the SNR of received signal. Factors, which affect the received signal in ultrasonic testing process, is analyzed, phase sensitivity detection technology is presented to process the received signal, and ultrasonic testing information can be acquired by the calculation of phase signal. The principle of super-heterodyne receiver and phase sensitivity detector is introduced; some problem which should be pay attention in ultrasonic testing process is explained. Based on the research result, air-coupled ultrasonic testing system with phase sensitivity detector is constituted. Experiment results of image testing on carbon fiber reinforced plastic plate indicated that, phase sensitivity detection technology can improved signal to noise ratio of system and testing effect in evidence.

scholarly journals Improvement of CO2-DIAL Signal-to-Noise Ratio Using Lifting Wavelet Transform

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2362 ◽  
Author(s):  
Chengzhi Xiang ◽  
Ge Han ◽  
Yuxin Zheng ◽  
Xin Ma ◽  
Wei Gong
Keyword(s):  
Wavelet Transform ◽  
Carbon Cycle ◽  
Signal To Noise Ratio ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Signal Denoising ◽  
Differential Absorption ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Lifting Wavelet

Atmospheric CO2 plays an important role in controlling climate change and its effect on the carbon cycle. However, detailed information on the dynamics of CO2 vertical mixing remains lacking, which hinders the accurate understanding of certain key features of the carbon cycle. Differential absorption lidar (DIAL) is a promising technology for CO2 detection due to its characteristics of high precision, high time resolution, and high spatial resolution. Ground-based CO2-DIAL can provide the continuous observations of the vertical profile of CO2 concentration, which can be highly significant to gaining deeper insights into the rectification effect of CO2, the ratio of respiration photosynthesis, and the CO2 dome in urban areas. A set of ground-based CO2-DIAL systems were developed by our team and highly accurate long-term laboratory experiments were conducted. Nonetheless, the performance suffered from low signal-to-noise ratio (SNR) in field explorations because of decreasing aerosol concentrations with increasing altitude and surrounding interference according to the results of our experiments in Wuhan and Huainan. The concentration of atmospheric CO2 is derived from the difference of signals between on-line and off-line wavelengths; thus, low SNR will cause the superimposition of the final inversion error. In such a situation, an efficient and accurate denoising algorithm is critical for a ground-based CO2-DIAL system, particularly in field experiments. In this study, a method based on lifting wavelet transform (LWT) for CO2-DIAL signal denoising was proposed. This method, which is an improvement of the traditional wavelet transform, can select different predictive and update functions according to the characteristics of lidar signals, thereby making it suitable for the signal denoising of CO2-DIAL. Experiment analyses were conducted to evaluate the denoising effect of LWT. For comparison, ensemble empirical mode decomposition denoising was also performed on the same lidar signal. In addition, this study calculated the coefficient of variation (CV) at the same altitude among multiple original signals within 10 min and then performed the same calculation on the denoised signal. Finally, high-quality signal of ground-based CO2-DIAL was obtained using the LWT denoising method. The differential absorption optical depths of the denoised signals obtained via LWT were calculated, and the profile distribution information of CO2 concentration was acquired during field detection by using our developed CO2-DIAL systems.

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