scholarly journals A New Wavelet Threshold Function and Denoising Application

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Lu Jing-yi ◽  
Lin Hong ◽  
Ye Dong ◽  
Zhang Yan-sheng
Keyword(s):  
Threshold Value ◽  
Threshold Function ◽  
Basic Principles ◽  
Threshold Functions ◽  
Fixed Threshold ◽  
Adjustment Factors ◽  
Soft Threshold ◽  
Traditional Structures ◽  
Mean Square Errors ◽  
Constant Deviation

In order to improve the effects of denoising, this paper introduces the basic principles of wavelet threshold denoising and traditional structures threshold functions. Meanwhile, it proposes wavelet threshold function and fixed threshold formula which are both improved here. First, this paper studies the problems existing in the traditional wavelet threshold functions and introduces the adjustment factors to construct the new threshold function basis on soft threshold function. Then, it studies the fixed threshold and introduces the logarithmic function of layer number of wavelet decomposition to design the new fixed threshold formula. Finally, this paper uses hard threshold, soft threshold, Garrote threshold, and improved threshold function to denoise different signals. And the paper also calculates signal-to-noise (SNR) and mean square errors (MSE) of the hard threshold functions, soft thresholding functions, Garrote threshold functions, and the improved threshold function after denoising. Theoretical analysis and experimental results showed that the proposed approach could improve soft threshold functions with constant deviation and hard threshold with discontinuous function problems. The proposed approach could improve the different decomposition scales that adopt the same threshold value to deal with the noise problems, also effectively filter the noise in the signals, and improve the SNR and reduce the MSE of output signals.

The Application of the Improved Threshold Method in Wavelet Image De-Noising

2014 ◽  
Vol 1039 ◽  
pp. 280-285
Author(s):  
Guang Hua Chen ◽  
Gui Hong Yan
Keyword(s):  
Threshold Value ◽  
Threshold Function ◽  
Threshold Values ◽  
Wavelet Shrinkage ◽  
Threshold Method ◽  
Scale Levels ◽  
Soft Threshold ◽  
Simulation Results ◽  
Wavelet Image ◽  
Constant Deviation

The key of wavelet image threshold de-noising is the choice of the threshold function and the threshold value. To overcome the shortcomings of constant deviation existing between estimated wavelet coefficients and decomposition coefficients in the soft threshold function and discontinuity of the hard threshold function, a new threshold function based on wavelet shrinkage in image de-noising is presented in this paper. Threshold values of images with different edges and texture degrees are fine-tuned when the threshold value is set. Furthermore, a self-adaption optimal threshold which is fit to all scale levels is designed based on features of multiscale and multiresolution of wavelet transform. Simulation results show that the proposed methods are efficient to reduce the noise while preserving the detail information of the image.

Improved Wavelet Thresholding for CCD Measurement Image Denoising

2014 ◽  
Vol 574 ◽  
pp. 432-435 ◽  
Author(s):  
Jie Zhan ◽  
Zhen Xing Li
Keyword(s):  
Image Denoising ◽  
Threshold Function ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Local Correlation ◽  
Thresholding Method ◽  
Threshold Functions ◽  
Soft Threshold ◽  
Simulation Results ◽  
Filtering Effect

An improved wavelet thresholding method is presented and successfully applied to CCD measuring image denoising. On the analysis of the current widely used soft threshold and hard threshold, combining characteristics of the CCD measuring image and use of local correlation of wavelet coefficients, an improved threshold function is proposed, and the denoising results were contrasted among different threshold functions. The simulation results show that adopting the improved threshold function can acquire better filtering effect than traditional soft threshold and hard threshold methods.

Wavelet De-Noising Method Based on a New Kind of Threshold Function

2014 ◽  
Vol 519-520 ◽  
pp. 1057-1060
Author(s):  
Fang Liu ◽  
Qi Xie ◽  
Wei Ge Liang ◽  
Wei Yi Chen
Keyword(s):  
Polynomial Interpolation ◽  
Interpolation Method ◽  
Wavelet Coefficient ◽  
Threshold Function ◽  
Original Signal ◽  
High Order Derivative ◽  
Advantages And Disadvantages ◽  
Soft Threshold ◽  
Simulation Results ◽  
Constant Deviation

Based on wavelet threshold de-noising method which put forward by Donoho, we analyze and compare the advantages and disadvantages of hard threshold, soft threshold and some improved threshold methods. Based on polynomial interpolation method, a new threshold function is proposed, which is continuous in the whole threshold area, with little constant deviation between the estimated wavelet coefficients and original signal wavelet coefficient, and high-order derivative and easy processing. The simulation results show that this method can give better optical effect, SNR gains and MSE performance.

Adaptive Denoising Algorithms Based on Wavelet for Pool Underwater Image

2013 ◽  
Vol 333-335 ◽  
pp. 1024-1029 ◽  
Author(s):  
Yi Wang ◽  
Fei Lei ◽  
Guang Jie Fu
Keyword(s):  
Threshold Value ◽  
Threshold Function ◽  
Time Cost ◽  
Forward Simulation ◽  
Threshold Functions ◽  
Underwater Image ◽  
Life Saving ◽  
Simulation Results ◽  
Adaptive Denoising ◽  
Intelligent Life

Image denoising is an important step in image processing of pools intelligent life-saving system, adaptive denoising algorithms has based on wavelet in this paper. Firstly, the threshold value selection was discussed and then the effect of threshold functions on denoising result was evaluated. Finally, an improved adaptive nonlinear threshold function was put forward. Simulation results showing that the adaptive denoising algorithm designed in this paper can achieve higher PSNR value, and time cost can be satisfy the real-time demand of pool intelligent life-saving system simultaneously.

Application of an Improved Algorithm for Denoising of Wavelet Threshold in Palmprint Recognition

2014 ◽  
Vol 536-537 ◽  
pp. 222-225
Author(s):  
Jing Wen Li ◽  
Shan Hong Yang
Keyword(s):  
Feature Extraction ◽  
Image Denoising ◽  
Common Method ◽  
Threshold Function ◽  
Palmprint Recognition ◽  
Threshold Functions ◽  
Image Denosing ◽  
Soft Threshold ◽  
Improved Function ◽  
Better Than

In the acquisition process of palmprint image, the image is often subject to interference from the outside noise.This noise will affect the future of Palmprint feature extraction. Denoising of wavelet is a common method for denoising of palmprint image. The defect of traditional soft and hard threshold functions in image denoising is analyzed. An improved threshold function is proposed on the basis of soft and hard threshold functions. The conventional hard, soft threshold functions and the improved function are used respectively in image denosing. Experiments prove that the improved wavelet threshold function is better than conventional soft, hard threshold function in denoising effect.

An appropriate thresholding method of wavelet denoising for dropping ambient noise

2018 ◽  
Vol 16 (03) ◽  
pp. 1850012 ◽  
Author(s):  
Kim Kyong Il ◽  
Ri Ui Hwan ◽  
Chon Bong Pil
Keyword(s):  
Theoretical Analysis ◽  
Ambient Noise ◽  
Threshold Value ◽  
Wavelet Denoising ◽  
Threshold Function ◽  
Discrete Wavelet ◽  
Thresholding Method ◽  
Threshold Functions ◽  
Useful Signal ◽  
Non Stationary Signal

For the non-stationary signal denoising, an effective method for dropping ambient noise is based on discrete wavelet transform. Also, in order to minimize the loss of useful signal and get high SNR in the wavelet denoising, it is very important that the thresholding is suitable for the characteristics of signal. In this paper, we propose new thresholding method to reduce an ambient noise and to detect effectively the useful signal. First, we analyze four kinds of previous wavelet threshold functions (Hard, Soft, Garrote and Hyperbola) and propose new wavelet threshold function compromised between Garrote and Hyperbola threshold functions. Next, a threshold value is selected by value to reduce exponentially according to the wavelet decomposition level. We also analyze a continuity and monotonicity, and prove the logic of new threshold function. The results of theoretical analysis show that new threshold function solves the problems of constant error and discontinuity of previous threshold functions, and minimizes the information loss of useful signal. The results of experiment show that SNR of new thresholding method is highest and RMSE and Entropy are smallest. The results of theoretical analysis and experiment show that new thresholding method is more appropriate to wavelet denoising for dropping ambient noise than previous methods.

scholarly journals Empirical Mode Decomposition De-Noising Method Based on Improved Soft-Threshold

2011 ◽  
Vol 1 ◽  
pp. 421-425 ◽  
Author(s):  
Jian Hui Xi ◽  
Jia Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Soft Threshold ◽  
Simulation Results ◽  
Improved Function ◽  
Constant Deviation

In this paper, an improved soft-threshold function is constructed, combined the improved function and empirical mode decomposition (EMD) methods, a new de-noising method has been proposed. Set the adaptive threshold for the intrinsic mode functions (IMFs) of the EMD, and then de-noise the each IMF respectively. Finally, the de-noised signal is reconstructed by the de-noised IMF components. Through the simulation results of quantitative analysis by signal-to-noise ratio (SNR) and mean square error (MSE), the algorithm in this paper has better de-noising effect. Also, this method can effectively improve the constant deviation between the original signal and the de-noised signal by traditional soft-threshold.

Wavelet Image Denoising Based on the New Threshold Function

2013 ◽  
Vol 347-350 ◽  
pp. 2231-2235
Author(s):  
Hui Tang ◽  
Zeng Li Liu ◽  
Lin Chen ◽  
Zai Yu Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Decomposition Level ◽  
Denoising Method ◽  
Global Threshold ◽  
Derivative Discontinuity ◽  
Soft Threshold ◽  
Wavelet Image ◽  
Constant Deviation

A new threshold function was proposed to overcome that hard threshold function is not continuous, soft threshold function has constant deviation and derivative discontinuity defects. It will be applied to using different thresholds denoising method with different decomposition level based on the D.J global threshold. Experimental results shows that the denoising result of new threshold function is superior to the traditional soft and hard threshold function in minimum mean square error (MSE) and peak signal to noise ratio (PSNR).

scholarly journals Seismic random noise attenuation using modified wavelet thresholding

2017 ◽  
Vol 59 (6) ◽  
Author(s):  
Qi-sheng Zhang ◽  
Jin-juan Jiang ◽  
Jin-hai Zhai ◽  
Xin-yue Zhang ◽  
Yi-jun Yuan ◽  
...  
Keyword(s):  
Random Noise ◽  
Seismic Exploration ◽  
Threshold Function ◽  
Laboratory Simulation ◽  
Noise Attenuation ◽  
Wavelet Thresholding ◽  
Random Noise Attenuation ◽  
Threshold Functions ◽  
Convolution Model ◽  
Mean Square Errors

<p>In seismic exploration, random noise deteriorates the quality of acquired data. This study analyzed existing denoising methods used in seismic exploration from the perspective of random noise. Wavelet thresholding offers a new approach to reducing random noise in simulation results, synthetic data, and real data. A modified wavelet threshold function was developed by considering the merits and demerits of conventional soft and hard thresholding schemes. A MATLAB (matrix laboratory) simulation model was used to compare the signal-to-noise ratios (SNRs) and mean square errors (MSEs) of the soft, hard, and modified threshold functions. The results demonstrated that the modified threshold function can avoid the pseudo-Gibbs phenomenon and produce a higher SNR than the soft and hard threshold functions. A seismic convolution model was built using seismic wavelets to verify the effectiveness of different denoising methods. The model was used to demonstrate that the modified thresholding scheme can effectively reduce random noise in seismic data and retain the desired signal. The application of the proposed tool to a real raw seismogram recorded during a land seismic exploration experiment located in north China clearly demonstrated its efficiency for random noise attenuation.</p>

EMG Signal De-Noising Based on Wavelet Packet μ Rhythm Threshold Method

2013 ◽  
Vol 394 ◽  
pp. 560-565 ◽  
Author(s):  
Gui Liang Chen ◽  
Guang Xu Wang ◽  
Geng Qian Liu
Keyword(s):  
Wavelet Packet ◽  
Threshold Function ◽  
Threshold Method ◽  
Semg Signal ◽  
Physiological Noise ◽  
Emg Signal ◽  
Before And After ◽  
Soft Threshold ◽  
Constant Deviation ◽  
Μ Rhythm

Surface Electromyographic (SEMG) signal is characterized by physiological noise contained,in order to eliminate inclusion noise. In wavelet packet analysis domain,after analysising the traditional soft-threshold and hard-threshold de-noising method of characteriscs,a new method which is μ rhythm threshold method is provided to improve threshold function. In Matlab7.0,the relevant procedures and simulation show that the method can not only solve the hard-threshold de-noising continuous problem,but also solve the detects before and after the threshold treatment of the soft-threshold wavelet coefficients constant deviation. Meanwhile,the application of the method in the wavelet analysis shows that wavelet packet μ rhythm threshold method de-noising significantly better than wavelet μ rhythm threshold method,and proves that the wavelet packet has remarkable capacity to de-noising.

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