Image Enhancement Method for Steel Surface Defects Based on DT-CWT

2013 ◽  
Vol 380-384 ◽  
pp. 3686-3689
Author(s):  
Zhi Xin Chen ◽  
Ping Wang ◽  
Shi Kun Xie
Keyword(s):  
Surface Defects ◽  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Complex Wavelet Transform ◽  
Enhancement Method ◽  
Shift Invariance ◽  
Gaussian Probability Distribution ◽  
Simulation Results ◽  
Non Gaussian ◽  
Better Than

A method based on Dual-Tree Complex Wavelet Transform (DT-CWT) was proposed for enhancing the images. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking advantage of near shift-invariance of DT-CWT, it can obtain higher signal-to-noise ratio (SNR) than common wavelet denoising methods. The simulation results show that the proposed method is better than the traditional methods. It has a good enhancement performance which can improve the details of the image automatically.

Signal Denoising of MEMS Microstructure Profile

2008 ◽  
Vol 381-382 ◽  
pp. 69-72
Author(s):  
Kai Hu ◽  
Xiang Qian Jiang ◽  
Xiao Jun Liu
Keyword(s):  
Wavelet Transform ◽  
Probability Distribution ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Complex Wavelet Transform ◽  
Shift Invariance ◽  
Gaussian Probability Distribution ◽  
Feature Information ◽  
Visual Artifacts ◽  
Non Gaussian

A new signal-denoising approach based on DT-CWT (Dual-Tree Complex Wavelet Transform) is presented in this paper to extract feature information from microstructure profile. It takes advantage of shift invariance of DT-CWT, non-Gaussian probability distribution for the wavelet coefficients and the statistical dependencies between a coefficient and its parent. This approach substantially improved the performance of classical wavelet denoising algorithms, both in terms of SNR and in terms of visual artifacts. A simulated MEMS microstructure signal is analyzed.

Attenuation of noise and simultaneous source interference using wavelet denoising

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. V179-V190 ◽  
Author(s):  
Zhou Yu ◽  
Ray Abma ◽  
John Etgen ◽  
Claire Sullivan
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Wavelet Denoising ◽  
Complex Wavelet Transform ◽  
Low Velocity ◽  
Noise Characteristics ◽  
Interference Noise ◽  
Seismic Image ◽  
Simultaneous Source

High-resolution seismic imaging requires noise attenuation to achieve signal-to-noise ratio (S/N) improvements without compromising data bandwidth. Amplitude versus offset analysis requires good amplitude fidelity in premigration processes. Any nonreflected wavefield energy in the data will degrade the seismic image quality. Despite significant progress over the years, preserving low-frequency signals without compromising the S/N and avoiding the smearing of aliased signal are still a challenge for conventional methods. This problem is compounded when additional interference noise is added with simultaneous source acquisition. Because noise characteristics vary from shot to shot and receiver to receiver, we need a method that is robust and effective. In addition, we also want the method to be efficient and easy to use from a practical perspective. We have recently developed an approach using a wavelet transform to deterministically separate the primary signal from the noise, including simultaneous source interference. The goals are (1) improving the S/N without compromising bandwidth, (2) preserving the low-frequency and near-offset primaries without compromising the S/N, and (3) preserving the local primary wavefield while attenuating noise. For distance-separated simultaneous source acquisition, the goal is preserving long-offset primaries while removing interference. This wavelet denoising flow consists of a linear transformation and filtering using the complex wavelet transform (CWT). For reflection signals, normal moveout (NMO) is used. NMO transforms the low-velocity surface waves and the interference noise to where it is easily identified and rejected with a dip filter in the multidimensional CWT domain. Land field data examples have demonstrated significantly improved S/Ns and low-frequency signal preservation in migrated images after wavelet denoising. Since the numerical implementation of the CWT is as fast as a fast Fourier transform, this flow is able to suppress noise and interference simultaneously on the 3D land data much faster than the other inversion methods.

scholarly journals Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

2020 ◽  
Author(s):  
Yu Wang ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  
Fumiyuki Adachi
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Time Varying ◽  
Signal Modulation ◽  
Generalization Capability ◽  
Common Features ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
Non Gaussian

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

Performance Enhancement of Atmospheric Coherent Optical Communication Systems Using DPSK

2012 ◽  
Vol 263-266 ◽  
pp. 1081-1084
Author(s):  
Yu Peng Li ◽  
Yan Gan Zhang ◽  
Xue Guang Yuan ◽  
Jin Nan Zhang ◽  
Ming Lun Zhang ◽  
...  
Keyword(s):  
Atmospheric Turbulence ◽  
Optical Communication ◽  
Communication Systems ◽  
Performance Enhancement ◽  
Signal To Noise Ratio ◽  
Free Space Optical ◽  
Eye Diagram ◽  
Optical Communication Systems ◽  
Simulation Results ◽  
Better Than

A coherent DPSK transmission system is presented to improve the receiver sensitivity for free space optical (FSO) communication. The coherent DPSK is an effective way to overcome the atmospheric turbulence. The eye diagram and bit error rate (BER) of the system are got by the simulation. Results show that with the coherent reception method, coherent DPSK offers improved signal-to-noise ratio (SNR) performance compared to the DPSK without coherent and OOK format. And hence it can be effective to overcome the signal impairment caused by atmospheric turbulence. It is shown that the sensitivity of the system employing coherent DPSK is 3dB better than a comparable system using DPSK without coherent and 9dB better than a system using OOK.

Performance study of multistage of 40 kb/s ADPCM systems

2015 ◽  
Vol 6 (2) ◽  
pp. 85-88
Author(s):  
M. Al-Rawi
Keyword(s):  
Signal To Noise Ratio ◽  
Differential Pulse ◽  
Data Rate ◽  
Performance Study ◽  
Pulse Code Modulation ◽  
Signal To Noise ◽  
Differential Pulse Code Modulation ◽  
Code Modulation ◽  
Simulation Results ◽  
Better Than

The contribution of this paper is that the measure of the performance of multistage of 40 kb/s Adaptive Differential Pulse Code Modulation (ADPCM) using signal-to-noise-ratio formula previously derived by AL-Rawi. The multistage performance is tested using QAM signal at data rate of 9.6 kb/s with four types of constellations, rectangular, and (5,11), (4,12), (8,8) circular. The simulation results show that the performance degrades with increasing the number of stages of ADPCM. Also, the performance with circular constellation is better than that with rectangular one.

scholarly journals Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

2020 ◽  
Author(s):  
Yu Wang ◽  
Guan Gui ◽  
Tomoaki Ohtsuki ◽  
Fumiyuki Adachi
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Modulation Classification ◽  
Time Varying ◽  
Signal Modulation ◽  
Generalization Capability ◽  
Common Features ◽  
Automatic Modulation Classification ◽  
Simulation Results ◽  
Non Gaussian

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

scholarly journals A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS

2012 ◽  
pp. 212-217
Author(s):  
T. Muni Kumar ◽  
M.B.Rama Murthy ◽  
Ch.V.Rama Rao ◽  
K.Srinivasa Rao
Keyword(s):  
Speech Enhancement ◽  
Weighting Factor ◽  
Wiener Filtering ◽  
Noise Masking ◽  
Enhancement Technique ◽  
Enhancement Method ◽  
Simulation Results ◽  
Spectral Weighting ◽  
Better Than

This paper deals with musical noise result from perceptual speech enhancement type algorithms and especially wiener filtering. Although perceptual speech enhancement methods perform better than the non perceptual methods, most of them still return annoying residual musical noise. This is due to the fact that if only noise above the noise masking threshold is filtered then noise below the noise masking threshold can become audible if its maskers are filtered. It can affect the performance of perceptual speech enhancement method that process audible noise only. In order to overcome this drawback here proposed a new speech enhancement technique. It aims to improve the quality of the enhanced speech signal provided by perceptual wiener filtering by controlling the latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results shows that the performance is improved compared to other perceptual speech enhancement methods

scholarly journals An Improved Infrared/Visible Fusion for Astronomical Images

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Attiq Ahmad ◽  
Muhammad Mohsin Riaz ◽  
Abdul Ghafoor ◽  
Tahir Zaidi
Keyword(s):  
State Of The Art ◽  
Invariance Property ◽  
Complex Wavelet Transform ◽  
Noise Effects ◽  
Distance Transforms ◽  
Local Standard ◽  
Shift Invariance ◽  
Simulation Results ◽  
Astronomical Images ◽  
Fusion Scheme

An undecimated dual tree complex wavelet transform (UDTCWT) based fusion scheme for astronomical visible/IR images is developed. The UDTCWT reduces noise effects and improves object classification due to its inherited shift invariance property. Local standard deviation and distance transforms are used to extract useful information (especially small objects). Simulation results compared with the state-of-the-art fusion techniques illustrate the superiority of proposed scheme in terms of accuracy for most of the cases.

scholarly journals Simulation and Nonlinearity Optimization of a High-Pressure Sensor

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4419
Author(s):  
Ting Li ◽  
Haiping Shang ◽  
Weibing Wang
Keyword(s):  
High Pressure ◽  
Pressure Sensor ◽  
Pressure Sensors ◽  
Original Model ◽  
Ansys Software ◽  
Nonlinear Error ◽  
Sensor Model ◽  
Optimized Model ◽  
Simulation Results ◽  
Better Than

A pressure sensor in the range of 0–120 MPa with a square diaphragm was designed and fabricated, which was isolated by the oil-filled package. The nonlinearity of the device without circuit compensation is better than 0.4%, and the accuracy is 0.43%. This sensor model was simulated by ANSYS software. Based on this model, we simulated the output voltage and nonlinearity when piezoresistors locations change. The simulation results showed that as the stress of the longitudinal resistor (RL) was increased compared to the transverse resistor (RT), the nonlinear error of the pressure sensor would first decrease to about 0 and then increase. The theoretical calculation and mathematical fitting were given to this phenomenon. Based on this discovery, a method for optimizing the nonlinearity of high-pressure sensors while ensuring the maximum sensitivity was proposed. In the simulation, the output of the optimized model had a significant improvement over the original model, and the nonlinear error significantly decreased from 0.106% to 0.0000713%.

scholarly journals Injection-rolling nozzle in an imprint system with continuous injection direct rolling for ultra-thin microstructure guide light plate

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110112
Author(s):  
Yan Lou ◽  
Kewei Chen ◽  
Xiangwei Zhou ◽  
Yanfeng Feng
Keyword(s):  
Flow Velocity ◽  
Superior Performance ◽  
Light Transmittance ◽  
Velocity Difference ◽  
Orthogonal Experiments ◽  
Width Direction ◽  
Direct Rolling ◽  
Simulation Results ◽  
Continuous Injection ◽  
Better Than

A novel Injection-rolling Nozzle (IRN) in an imprint system with continuous injection direct rolling (CIDR) for ultra-thin microstructure polymer guide light plates was developed to achieve uniform flow velocity and temperature at the width direction of the cavity exit. A novel IRN cavity was designed. There are eight of feature parameters of cavity were optimized by orthogonal experiments and numerical simulation. Results show that the flow velocity at the width direction of the IRN outlet can reach uniformity, which is far better than that of traditional cavity. The smallest flow velocity difference and temperature difference was 0.6 mm/s and 0.24 K, respectively. The superior performance of the IRN was verified through a CIDR experiment. Several 0.35-mm thick, 340-mm wide, and 10-m long microstructural Polymethyl Methacrylate (PMMA) guide light plates were manufactured. The average filling rates of the microgrooves with the aspect ratio 1:3 reached above 93%. The average light transmittance is 88%.

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