Catenary image denoising method using lifting wavelet-based contourlet transform with cycle shift-invariance

2019 ◽  
Vol 17 (01) ◽  
pp. 1850060 ◽  
Author(s):  
Changdong Wu ◽  
Hua Jiang
Keyword(s):  
Image Denoising ◽  
Mean Squared Error ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Contourlet Transform ◽  
Signal To Noise ◽  
Denoising Method ◽  
Shift Invariance ◽  
Lifting Wavelet ◽  
Noise Ratio

In the catenary status detection system based on the image processing, quality of the captured catenary image is critical. In order to obtain a high quality image for further analysis, this paper proposes a new catenary image denoising method based on lifting wavelet-based contourlet transform with cycle shift-invariance (LWBCTCS). In this method, the lifting wavelet is first constructed based on wavelet transform (WT). Then, to decrease the redundancy of contourlet transform (CT), the lifting wavelet-based contourlet transform (LWBCT) is built by using the lifting wavelet to replace the Laplacian pyramid (LP) transform of CT. Finally, the LWBCT with the cycle shift-invariance (LWBCTCS) algorithm is combined to reduce the pseudo-Gibbs phenomena of LWBCT. The proposed method not only has the virtues of multi-scale and multi-direction, but also reduces the visual artifacts in the denoised image. The results of comparative experiments with captured catenary image show that the proposed method can achieve satisfactory denoising performance, in particular, for catenary image with abundant texture and detail outline information. It not only eliminates noise but also preserves the textures and details simultaneously. Besides, comprehensive consideration of the denoising performance shows that the proposed algorithm in terms of the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and mean squared error (MSE) is stable than those conventional denoising algorithms, including WT, CT, curvelet transform (CV) and BLS-GSM methods. The visual quality as well as quantitative metrics is superior than those conventional denoising methods.

Application of Wavelet Method on Preprocessing of Voltage Signals

2010 ◽  
Vol 40-41 ◽  
pp. 272-276
Author(s):  
Li Di Wang ◽  
Nan Zhu ◽  
Jin Kai Li
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Electrical Parameter ◽  
Wavelet Denoising ◽  
Signal To Noise ◽  
Denoising Method ◽  
Wavelet Method ◽  
Squared Error ◽  
Noise Ratio

Wavelet denoising method is applied in the measurement voltage signals in this paper. Noise reduction is important for signal preprocessing in order to achieve many objects such as the improvement of accuracy of modal analysis and electrical parameter identification, the effective extraction of features and auto-matic classification of different kinds of signals. The voltage signals measured from one 35Kv bus are used for the preprocessing research. The denoising effect is evaluated by three parameters, i.e. signal to noise ratio, mean squared error, and capture ability of step points. Compared with the traditional methods including mean filtering and medial filtering, wavelet method is superior in signal to noise ratio and mean squared error.

Comparison between Mean Filter and Median Filter Algorithm in Image Denoising Field

2014 ◽  
Vol 644-650 ◽  
pp. 4112-4116 ◽  
Author(s):  
Xiao Xin Sun ◽  
Wei Qu
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Response Characteristic ◽  
Fast Response ◽  
High Signal ◽  
Signal To Noise ◽  
Denoising Method ◽  
Mean Filter ◽  
Noise Ratio

An image denoising method based on spatial filtering is proposed on order to overcoming the shortcomings of traditional denoising methods in this paper. The method combined mean mask algorithm with median filtering technique is able to replace the gray values of noisy image pixel by the mean or median value in its neighborhood mask matrix and highlight the characteristic value of the image. Peak signal to noise ratio and mean square error are used as the evaluation index in this method and comparison between mean filter and median filter is done. The experimental results show that this denoising system makes the images have a high signal to noise ratio and integrity of edge details and take into account real-time, and fast response characteristic of the system.

scholarly journals Image Denoising by using Modified SGHP Algorithm

2018 ◽  
Vol 8 (2) ◽  
pp. 971
Author(s):  
Sreedhar Kollem ◽  
K. Ramalinga Reddy ◽  
D. Sreenivasa Rao
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Natural Images ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Denoising Method ◽  
Noisy Observation ◽  
Noise Ratio

In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

Research on LIFT Technology for 3D Seismic Denoising in Coalfield

2014 ◽  
Vol 556-562 ◽  
pp. 6328-6331
Author(s):  
Su Zhen Shi ◽  
Yi Chen Zhao ◽  
Li Biao Yang ◽  
Yao Tang ◽  
Juan Li
Keyword(s):  
Signal To Noise Ratio ◽  
High Signal ◽  
3D Seismic ◽  
Signal To Noise ◽  
Denoising Method ◽  
Seismic Processing ◽  
Work Area ◽  
Noise Data ◽  
Noise Ratio ◽  
Effective Signal

The LIFT technology has applied in process of denoising to ensure the imaging precision of minor faults and structure in 3D coalfield seismic processing. The paper focused on the denoising process in two study areas where the LIFT technology is used. The separation of signal and noise is done firstly. Then denoising would be done in the noise data. The Data of weak effective signal that is from the noise data could be blended with the original effective signal to reconstruct the denoising data, so the result which has high signal-to-noise ratio and preserved amplitude is acquired. Thus the fact shows that LIFT is an effective denoising method for 3D seismic in coalfield and could be used widely in other work area.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

2020 ◽  
Vol 20 (03) ◽  
pp. 2050025
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja ◽  
A. Kasthuri
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Signal To Noise ◽  
Coupled Neural Networks ◽  
Noise Ratio ◽  
Pulse Coupled Neural Networks ◽  
Image Quality Index

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.

A constrained minimum power adaptive beamformer with time‐varying adaptation rate

Geophysics ◽  
1979 ◽  
Vol 44 (6) ◽  
pp. 1088-1096 ◽  
Author(s):  
Wen‐Wu Shen
Keyword(s):  
Adaptive Algorithm ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
High Amplitude ◽  
Research Station ◽  
Power Performance ◽  
Signal To Noise ◽  
Filter Output ◽  
Short Period ◽  
Noise Ratio

A linear adaptive algorithm was developed for array beamforming purposes. The design goal for the algorithm is to minimize the squared filter output subject to filter constraints which allow energy propagating from the array steering direction to pass without being distorted. The adaptive filter coefficients were initialized to satisfy the constraints which were preserved during the iterations. The adaptation rate is inversely varied with filter output and total input channel power. Performance of the algorithm was studied using the recorded short‐period array data from the Korean Seismic Research Station. Processed were a high‐amplitude signal from Kamchatka, a medium‐amplitude signal from eastern Kazakh, and a number of low‐amplitude signals from central Eurasia. Results of signal‐to‐noise ratio gain relative to a conventional beamformer among the events tested were consistent and were in the range of 4.5 to 6.5 dB in the wide passband. Much better signal‐to‐noise ratio improvement was obtained in the low‐frequency passband. The adaptive algorithm was programmed in the real‐time mode and can be implemented in a front‐end detection system.

Quantitative Analysis of Noise Impact on Duffing Chaotic Detection System Using Lyapunov Characteristic Exponents

2011 ◽  
Vol 130-134 ◽  
pp. 1331-1337
Author(s):  
Wen Jing Hu ◽  
Zhi Zhen Liu ◽  
Zhi Hui Li
Keyword(s):  
Detection System ◽  
Signal To Noise Ratio ◽  
Duffing Oscillator ◽  
Threshold Value ◽  
System State ◽  
Signal To Noise ◽  
Phase Trajectories ◽  
Lyapunov Characteristic Exponents ◽  
Noise Ratio ◽  
System States

Performance of the Duffing oscillator to detect weak signals buried in heavy noise is analyzed quantitatively by LCEs. First in the case of noise, differential equations to compute LCE s are derived using RHR algorithm, so the quantitative criteria to identify system states are obtained. Then using LCEs, the threshold value of the forced periodic term is found accurately. Finally the system state and state change are analyzed using LCEs by keeping the threshold value and varying the noise intensity, and the minimum signal to noise ratio is determined. By contrast of phase trajectories and LCEs, it shows that phase trajectories disturbed by strong noise sometimes are ambiguous to our eyes, but through LCEs, the system state can be identified clearly and quantitatively especially in strong noise background. So the minimum signal to noise ratio can be obtained accurately.

scholarly journals Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

2021 ◽  
Vol 23 (2) ◽  
pp. C44-C53
Author(s):  
Hanan A. R. Akkar ◽  
Wael A. H. Hadi ◽  
Ibraheem H. Al-Dosari ◽  
Saadi M. Saadi ◽  
Aseel Ismael Ali
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Leak Detection ◽  
The Novel ◽  
Denoising Method ◽  
System A ◽  
New Novel ◽  
Water Pipeline

The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance.

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