scholarly journals A Novel Method for the Enhancement of Composite Materials’ Terahertz Image Using Unsharp Masking and Guided Filtering Technology

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhilong Li ◽  
Jian Zuo ◽  
Yuanmeng Zhao ◽  
Zhongde Han ◽  
Zhihao Xu ◽  
...  
Keyword(s):  
Composite Materials ◽  
Internal Noise ◽  
Objective Evaluation ◽  
Terahertz Imaging ◽  
Discrete Wavelet ◽  
Local Contrast ◽  
Color Mapping ◽  
Unsharp Masking ◽  
Guided Filtering ◽  
Novel Method

When terahertz imaging technology is used for the nondestructive testing of composite materials, the signal is often affected by the experimental environment and internal noise of the system, as well as the absorption and scattering effect of the tested materials. The obtained image has degradation phenomena such as low contrast, poor resolution of small targets and blurred details. In order to improve the image quality, this paper proposes a novel method for the enhancement of composite materials’ terahertz image by using unsharp masking and guided filtering technology. The method includes the processing steps of hard threshold shrinkage denoising based on discrete wavelet transform, amplitude imaging, unsharp masking, guided filtering, contrast stretching, and pseudo-color mapping. In this paper, these steps are reasonably combined and optimized to obtain the final resulting image. To verify the effectiveness of the proposed method, a 150–220 GHz high frequency terahertz frequency modulated radar imaging system was used to image three commonly used sandwich structure composites, and the enhancement processing were carried out. The resulting images with significantly enhanced contrast, detail resolution and edge information were obtained, and the prefabricated defects were all detected; Five objective evaluation indexes including standard deviation, mean gradient, information entropy, energy gradient and local contrast were used to compare and analyze the processing results of different image enhancement methods. The subjective and objective evaluation results showed that the proposed method can effectively suppress the noise in terahertz detection signals, enhance the ability of defect detection and positioning, and improve the accuracy of detection. The proposed method in this paper is expected to play a positive role in improving the practicability of terahertz imaging detection technology and expanding its application fields.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

scholarly journals Implementation of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

2020 ◽  
Vol 9 (3) ◽  
pp. 3594-3603
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Signal To Noise Ratio ◽  
Objective Evaluation ◽  
Contourlet Transform ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Stationary Wavelet Transform ◽  
Signal To Noise ◽  
Different Levels

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation

scholarly journals Universal statistical steganalytic method

2017 ◽  
Vol 68 (2) ◽  
pp. 117-124
Author(s):  
Martin Broda ◽  
Vladimír Hajduk ◽  
Dušan Levický
Keyword(s):  
Wavelet Transformation ◽  
Ensemble Classifier ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Discrete Cosine Transformation ◽  
Static Images ◽  
Novel Method ◽  
Statistical Steganalysis ◽  
Popular Image

Abstract Novel image steganalytic method used to detection of secret message in static images is introduced in this paper. This method is based on statistical steganalysis (SS), where statistical vector is composed by 285 statistical features (parameters) extracted from DCT (Discrete Cosine Transformation) domain and 46 features extracted mainly from DWT (Discrete Wavelet Transformation) domain. Classification process was realized by Ensemble classifier that was helpful in reduction of computational and time complexity. Proposed steganalytic method was verified by detection of popular image steganographic methods. Novel method was also compared with existing steganalytic methods by overall detection accuracy of a secret message.

WATERMARK DETECTION USING DYNAMIC STOCHASTIC RESONANCE

2013 ◽  
Vol 12 (03) ◽  
pp. 1350010 ◽  
Author(s):  
RAJIB KUMAR JHA ◽  
APOORV CHATURVEDI ◽  
RAJLAXMI CHOUHAN
Keyword(s):  
Stochastic Resonance ◽  
Spread Spectrum ◽  
Internal Noise ◽  
External Noise ◽  
Detection Technique ◽  
Discrete Wavelet ◽  
Detection Techniques ◽  
Dynamic Stochastic Resonance ◽  
Watermark Embedding ◽  
Dynamic Stochastic

In this paper, a dynamic stochastic resonance (DSR) based watermark detection technique in discrete wavelet transform (DWT) domain is presented. Pseudo random bit sequence having certain seed value is considered as a watermark. Watermark embedding is done by embedding random bits in spread-spectrum fashion to the significant DWT coefficients. Watermark detection is quantitatively characterized by the value of correlation. The performance of watermark detection is improved by DSR which is an iterative process that utilizes the internal noise present in the image or external noise which is added during attacks. Even under various noise attacks, geometrical distortions, image enhancement and compression attacks, the DSR-based random bits detection is observed to give noteworthy improvement over existing watermark detection techniques. DSR-based technique is also found to give better detection performance when compared with the suprathreshold stochastic resonance-based detection technique.

Fault Location of Transmission Line Based on Discrete Wavelet Transforms

2011 ◽  
Vol 121-126 ◽  
pp. 1269-1273
Author(s):  
Wen Xiu Tang ◽  
Mo Zhang ◽  
Ying Liu ◽  
Xu Fei Lang ◽  
Liang Kuan Zhu
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Wavelet Transforms ◽  
Fault Location ◽  
Short Circuit ◽  
Signal Transmission ◽  
Transient Signal ◽  
Discrete Wavelet ◽  
Novel Method ◽  
Non Stationary Signal

In this paper, a novel method is investigated to detect short-circuit fault signal transmission lines in strong noise environment based on discrete wavelet transform theory. Simulation results show that the method can accurately determine the fault position, can effectively analyze the non-stationary signal and be suitable for transmission line fault occurred after transient signal detection. Furthermore, it can effectively eliminate noise effects of fault signal so as to realize the transmission lines of accurate fault.

A Novel Method to Extract Lip-Reading Features by Using LGEI and DWT

2014 ◽  
Vol 1079-1080 ◽  
pp. 820-823
Author(s):  
Li Guo Zheng ◽  
Mei Li Zhu ◽  
Qing Qing Wang
Keyword(s):  
Visual Speech ◽  
Second Step ◽  
Discrete Wavelet ◽  
Noise Resistance ◽  
Lip Reading ◽  
Novel Method ◽  
Reading System ◽  
Visual Speech Information ◽  
Speech Information ◽  
Precision Rate

This paper proposes a novel algorithm used in extraction of lip feature extraction for to improved efficiency and robustness of lip-reading system. First, Lip Gray Energy Image (LGEI) is used to smooth noise, and improve noise resistance of the system. Second, Discrete Wavelet Analysis (DWT) is used to extract salient visual speech information from lip by decorrelating spectral information. Last, lip features are obtained by downsampling data from second step, the resample can effectively reduce the amount of computation. Experimental results show the performance of this method is exceedingly discriminative, accurate and computation efficient, the precision rate can rate 96%.

scholarly journals Pseudocoloring Of Medical Images: A Research

2019 ◽  
Vol 8 (6) ◽  
pp. 3712-3716
Keyword(s):  
Entire Range ◽  
Medical Images ◽  
Target Image ◽  
Color Mapping ◽  
Size And Shape ◽  
Black And White ◽  
Grey Scale ◽  
Novel Method ◽  
The Creation ◽  
Color Map

A color mapping is a process by which we transfer the particular range of colors from source to the target image. This paper says about a novel method for medical images using pseudocolor by pre existence of color mapping. Conversion of grey scale image to color has no exact way or approach, direct way to colorize is tedious by which the entire range of colors can be changed, but in color mapping the conversion is done by converting the whole black and white image into color. This novel method presents black and white image to various colors. By the creation of color map that color map is applied for colorizing the medical grey scale images. This approach can be used for grey scale medical images irrespective of size and shape such that the intensity and aspect of the image can be improved

scholarly journals Noise Reduction for MEMS Gyroscope Signal: A Novel Method Combining ACMP with Adaptive Multiscale SG Filter Based on AMA

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4382 ◽  
Author(s):  
He ◽  
Sun ◽  
Wang
Keyword(s):  
Moving Average ◽  
Window Size ◽  
Superior Performance ◽  
Discrete Wavelet ◽  
Stopping Criterion ◽  
Mems Gyroscope ◽  
Smoothing Methods ◽  
Mode Decomposition ◽  
Novel Method ◽  
Micro Electromechanical System

In this paper, a novel hybrid method combining adaptive chirp mode pursuit (ACMP) with an adaptive multiscale Savitzky–Golay filter (AMSGF) based on adaptive moving average (AMA) is proposed for offline denoising micro-electromechanical system (MEMS) gyroscope signal. The denoising scheme includes preliminary denoising and further denoising. At the preliminary denoising stage, the original gyroscope signal is decomposed into signal modes one by one using ACMP with modified stopping criterion based on mutual information. Useful information is extracted while most noise is discarded in the residue at this stage. Then, AMSGF is proposed to further denoise the signal modes. Sample variance based on AMA is used to adjust the window size of AMSGF adaptively. Practical MEMS gyroscope signal denoising results under different motion conditions show the superior performance of the proposed method over empirical mode decomposition (EMD)-based denoising, discrete wavelet threshold denoising, and variational mode decomposition (VMD)-based denoising. Moreover, AMSGF is proven to gain a better denoising effect than some other common smoothing methods.

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