De-noising Effect Research Articles

Image Restoration Based on Stochastic Resonance in a Parallel Array of Fitzhugh–Nagumo Neuron

The poor denoising effect for noisy grayscale images with traditional processing methods would be obtained under strong noise condition, and some image details would be lost. In this paper, a parallel array model of Fitzhugh–Nagumo (FHN) neurons was proposed, which can restore noisy grayscale images well with low peak signal-to-noise ratio (PSNR) conditions and the image details are better preserved. Firstly, the row-column scanning method was used to convert the 2D grayscale image into a 1D signal, and then the 1D signal was converted into a binary pulse amplitude modulation (BPAM) signal by signal modulation. The modulated signal was input to an FHN parallel array for stochastic resonance (SR). Finally, the array output signal was restored to a 2D gray image, and the image restoration effect was analyzed based on the PSNR and Structural SIMilarity (SSIM) index. It is shown that the SR effect can be exhibited in an array of FHN neuron nonlinearities by increasing the array size, and the image restoration effect is significantly better than the traditional image restoration method, and larger PSNR and SSIM can be obtained. It provides a new idea for grayscale image restoration in a low PSNR environment.

A Tensor Subspace Representation-Based Method for Hyperspectral Image Denoising

In hyperspectral image (HSI) denoising, subspace-based denoising methods can reduce the computational complexity of the denoising algorithm. However, the existing matrix subspaces, which are generated by the unfolding matrix of the HSI tensor, cannot completely represent a tensor since the unfolding operation will destroy the tensor structure. To overcome this, we design a novel basis tensor that is directly learned from the original tensor and present a tensor subspace representation (TenSR), which is a more authentic representation for delivering the intrinsic structure of the tensor than a matrix subspace representation. Equipped with the TenSR, we then propose a TenSR-based HSI denoising (TenSRDe) model, which simultaneously considers the low-tubal rankness of the HSI tensor and the nonlocal self-similarity of the coefficient tensor. Moreover, we develop an efficient proximal alternating minimization (PAM) algorithm to solve the proposed nonconvex model and theoretically prove that the algorithm globally converges to a critical point. Experiments implemented on simulated and real data sets substantiate the denoising effect and efficiency of the proposed method.

Evolutionary Denoising-Based Machine Learning for Detecting Knee Disorders

Surface electromyography (sEMG) is a non-invasive tool that can aid physiological assessment of knee disorders towards clinical interventions. Machine Learning (ML) is widely used to classify sEMG data to help with early detection of knee disorders; however, the inherent noise and the high non-linearity of sEMG signals make pattern recognition a challenging task. This study aims to partly overcome these challenges with existing ML-based classifiers by denoising sEMG signals further via an innovative two-fold evolutionary approach. A novel Genetic Algorithm-based denoising approach is applied to sEMG data to decrease the search space for pattern-related classification. Thereafter, the proposed denoising technique is coupled with an ML-based classifier to improve the discrimination between physiological and pathophysiological knee functions from sEMG data by optimising its hyperparameters too. Thus, the novel evolutionary approach serves two purposes. Firstly, it further reduces noise in sEMG signals via a new GA-based denoising technique to concurrently maximise mutual information and minimise entropy; secondly, it also enables the optimisation of the classifier’s hyperparameters. The classification performance of the resulting hybrid algorithm was validated using sEMG data on 144 subjects (67 patients with knee disorders, 77 healthy subjects) and was found higher (ACC = 99.57%, 95% CI: 99.47–99.66; AUC = 1, 95% CI: 0.98–1) than that of similar ML algorithms and published studies. The hybrid algorithm achieved the highest classification performance by leveraging an evolutionary approach for effective denoising and hyperparameter optimisation, whilst retaining the lowest computational cost. Thus, the proposed evolutionary denoising ML-based classifier is deemed an accurate and reliable decision support system to aid the detection of knee disorders.

Study on the Evolution Monitoring of Topographic and Hydrological Characteristics of Small Watershed Based on Remote Sensing and GIS

Soil and water conservation is one of the key measures to improve the ecological environment of small watersheds and maintain the healthy life of the region. With the current method used to monitor the evolution of topographic and hydrological characteristics of a small watershed, the edge keeping index of the image and the signal-to-noise ratio of the image are low, the monitoring results are inaccurate, and there are some problems such as low edge keeping ability, poor denoising effect, and low monitoring accuracy. A monitoring method based on remote sensing and GIS for the evolution of topographic and hydrological characteristics of small watersheds is proposed. The hyperspectral data was transformed by the Principal Component Analysis (PCA). Group principal component images, use sparse representation method based on adaptive dictionary and dual-tree complex wavelet transform method to denoise principal component images with a small amount of information were the objectives of the paper; also the use the multi-scale wavelet transform to detect image edge; build a binary model of a pixel, extract vegetation index, and terrain factor based on a binary model of the pixel, and realize terrain and hydrological characteristics of the small watershed. The experimental results show that the proposed method has the high edge-preserving ability, good denoising effect, and high monitoring accuracy.

Digital Elevation Models for topographic characterisation and flood flow modelling along low-gradient, terminal dryland rivers: A comparison of spaceborne datasets for the Río Colorado, Bolivia

Many dryland rivers are terminal systems, with small channels undergoing prominent downstream size reductions before ending on channelless floodplains, in wetlands, or at playa margins. Spaceborne Digital Elevation Models (DEMs) provide potential for assessing subtle topographic and hydrodynamic changes in these low-gradient, low-relief settings, but challenges are posed by limitations in vertical and horizontal accuracy. This study evaluates the use of different spaceborne DEMs for topographic characterisation and flood flow modelling of the low-gradient (<0.0006 m m−1) Río Colorado terminal system, Bolivia. A comparison between DEM and field dGPS elevation data (1290 measurement points) reveals that the TanDEM-X DEM 12 m (TDX-12 m) has a RMSE of 0.47 m, far less than those of other frequently used spaceborne DEMs such as ALOS RTC (4.58 m) and SRTM (6.02 m). For hydrodynamic modelling, TDX-12 m data were smoothed (adaptive filter and feature-preserving DEM smoothing) and upscaled. The smoothed TDX-12 m data were mosaiced with a dGPS data-derived river path, surveyed along a reach (mean width <30 m) with a prominent downstream size decrease. The methods enabled effective de-noising of the TDX-12 m data (RMSE 0.29 m) and resulted in a high linear regression correlation coefficient (0.75). HEC-RAS 2D modelling reveals that in the selected reach, overbank flooding starts in the downstream part when discharge is <18 m3/s, with flow initially spreading through crevasse channels and levee topographic lows. As discharge increases, flow spreads farther across the floodplain, ultimately forming connected floodplain flow in distal topographic lows. Satellite imagery and a derived water index indicate the same floodplain flow patterns as the modelling (critical success index 0.77). Wider use of DEMs based on TDX-12 m data for topographic characterization and flood flow modelling along relatively small, low-gradient terminal dryland rivers will result in many scientific and applied benefits.

Review and prospect: NMR spectroscopy denoising and reconstruction with low-rank Hankel matrices and tensors.

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acquisition time. Thus, improving the apparent signal-to-noise ratio and accelerating data acquisition became indispensable. In this review, we summarize the recent progress on low-rank Hankel matrix and tensor methods, which exploit the exponential property of free-induction decay signals, to enable effective denoising and spectra reconstruction. We also outline future developments that are likely to make NMR spectroscopy a far more powerful technique.

A [formula omitted] PDE model for effective denoising

In this paper, we have proposed a higher order non-linear PDE model containing diffusion terms of TV−L2 and TV−H−1 model for effective image denoising. Fourier spectral method for space and convexity splitting method for time have been used to solve the proposed PDE model. Stability analysis for the time discretized equation has been derived. Numerical experiment has been done on some test images and the results have been compared with the results of some existing models.

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy.

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

Least-squares diffraction imaging using shaping regularization by anisotropic smoothing

We have used least-squares migration to emphasize edge diffractions. The inverted forward-modeling operator is the chain of three operators: Kirchhoff modeling, azimuthal plane-wave destruction, and the path-summation integral filter. Azimuthal plane-wave destruction removes reflected energy without damaging edge-diffraction signatures. The path-summation integral guides the inversion toward probable diffraction locations. We combine sparsity constraints and anisotropic smoothing in the form of shaping regularization to highlight edge diffractions. Anisotropic smoothing enforces continuity along edges. Sparsity constraints emphasize diffractions perpendicular to edges and have a denoising effect. Synthetic and field data examples illustrate the effectiveness of the proposed approach in denoising and highlighting edge diffractions, such as channel edges and faults.

An improved Gaussian frequency domain sparse inversion method based on compressed sensing

The traditional compressed sensing method for improving resolution is realized in the frequency domain. This method is affected by noise, which limits the signal-to-noise ratio and resolution, resulting in poor inversion. To solve this problem, we improved the objective function that extends the frequency domain to the Gaussian frequency domain having denoising and smoothing characteristics. Moreover, the reconstruction of the sparse reflection coefficient is implemented by the mixed L1_L2 norm algorithm, which converts the L0 norm problem into an L1 norm problem. Additionally, a fast threshold iterative algorithm is introduced to speed up convergence and the conjugate gradient algorithm is used to achieve debiasing for eliminating the threshold constraint and amplitude error. The model test indicates that the proposed method is superior to the conventional OMP and BPDN methods. It not only has better denoising and smoothing effects but also improves the recognition accuracy of thin interbeds. The actual data application also shows that the new method can effectively expand the seismic frequency band and improve seismic data resolution, so the method is conducive to the identification of thin interbeds for beach-bar sand reservoirs.

Weighted Nuclear Norm Minimization-Based Regularization Method for Image Restoration

Regularization methods have been substantially applied in image restoration due to the ill-posedness of the image restoration problem. Different assumptions or priors on images are applied in the construction of image regularization methods. In recent years, matrix low-rank approximation has been successfully introduced in the image denoising problem and significant denoising effects have been achieved. Low-rank matrix minimization is an NP-hard problem and it is often replaced with the matrix’s weighted nuclear norm minimization (WNNM). The assumption that an image contains an extensive amount of self-similarity is the basis for the construction of the matrix low-rank approximation-based image denoising method. In this paper, we develop a model for image restoration using the sum of block matching matrices’ weighted nuclear norm to be the regularization term in the cost function. An alternating iterative algorithm is designed to solve the proposed model and the convergence analyses of the algorithm are also presented. Numerical experiments show that the proposed method can recover the images much better than the existing regularization methods in terms of both recovered quantities and visual qualities.

Research on Speech Signal Denoising Algorithm Based on Wavelet Analysis

In this paper, wavelet analysis is used to remove noise superimposed on the speech signal to achieve denoising of the speech signal. The prepared speech signal is superimposed with Gaussian white noise with different signal-to-noise ratios, and is removed by using a forced noise removal processing method and a given threshold denoising processing method; The wavelet soft and hard threshold denoising method is used to separately superimpose different Gaussian white noise speech signals, and the hard threshold and soft threshold denoising are respectively performed, and different wavelets are used for processing; Four threshold selection methods are used to denoise the noisy signal using different wavelets. The denoising effects of these methods are simulated in MATLAB software, and combined with the signal-to-noise ratio of the signal after denoising, the denoising effects of various methods are judged. Experimental results show that soft threshold denoising is slightly better than hard threshold denoising. Among the four threshold selection methods, Rigrsure rule denoising is the best. Although other methods are effective, they are not obvious.

Smooth model of blasting seismic wave signal denoising based on two-stage denoising algorithm

ABSTRACT In this paper, a two-stage denoising algorithm is proposed. Complementary ensemble empirical mode decomposition based on permutation entropy (CEEMD-PE) is carried out for the noisy monitoring signal in the first stage. Several denoising models are established according to the intrinsic mode function obtained by CEEMD-PE. An objective function considers both the smoothness of the denoising model and the similarity between the denoising model and the noisy monitoring signal is established, and the second stage denoising is realized by solving the objective function. The denoising model corresponding to the optimal solution of the objective function is the smooth denoising model. In order to verify the correctness of the two-stage denoising algorithm, the mixed simulation signal with noise is denoised, and based on the definition of signal-to-noise ratio, the effect of two-stage denoising is calculated. Finally, the algorithm is applied to the actual blasting seismic signal denoising processing. It is found that the proposed algorithm can not only reduce the noise interference but also retain the real part of the original signal while filtering the noise.

A review of airport dual energy X-ray baggage inspection techniques: Image enhancement and noise reduction.

In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.

Adaptive image denoising for speckle noise images based on fuzzy logic

AbstractSpeckle noise is a kind of ubiquitous noise in medical image, which will damage the texture structure of image and affect the analysis of image structure by doctors. Therefore, we propose an image denoising model based on fuzzy logic, which can eliminate speckle noise in the image well, improve the recognition of the image, and facilitate the acquisition of image information by doctors. The main work arrangement of the algorithm model is to design a membership function that can traverse the noise image and preprocess the noise image to make the image smooth. Then, a mask template of 5 × 5 is designed by the definition of g‐l calculus, and there is mainly an unknown parameter in this template. We design the functional relation between this parameter and the image gradient, which makes the model algorithm adaptive. Finally, the convolution operation is performed between the template and the smooth image. By comparison with the existing mainstream models, the overall denoising effect of this model is better than other models, and the relevant numerical indexes are better than other models. This model is an extension of the denoising model of fuzzy theory, which is beneficial to the future research and development.

Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion

Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.

Novel Approach for MRI Image Quality Analysis by Hybrid Preprocessing Techniques

Image pre-preparing methods are utilized to improve the nature of a image before handling into an application. This uses a little neighborhood of a pixel in an information image to get another splendor esteem in the yield image. These pre-preparing methods are likewise called as filtration and goals upgrade. The clinical image quality parameters are fundamentally clamor and goals. The fundamental goal of this paper is to improve the image quality by denoising and goals upgrade. A large portion of the imaging methods are corrupted by clamor. So as to protect the edges and form data of the clinical images, the effective denoising and an improved upgrade method is required. This paper focuses the normal, middle and wiener sifting for image denoising and an addition based Discrete Wavelet Transform (DWT) strategy for goals improvement. The presentation of these strategies is assessed utilizing Peak Signal to Noise Ratio (PSNR). From the outcomes, it uncovers that the effective denoising and goals upgrade method is basic for image pre-handling.

Effects of apodization smoothing and denoising on spectral fitting

PurposeVisual review of individual spectra in magnetic resonance spectroscopic imaging (MRSI) data benefits from the application of spectral smoothing; however, if this processing step is applied prior to spectral analysis this can impact the accuracy of the quantitation. This study aims to analyze the effect of spectral denoising and apodization smoothing on the quantitation of whole-brain MRSI data obtained at short TE. MethodsShort-TE MRSI data obtained at 3 T were analyzed with no spectral smoothing, following (i) Gaussian apodization with values of 1, 2, 4, 6, and 8 Hz, and (ii) denoising using principal component analysis (dnPCA) with 3 different values for the number of retained principal components. The mean lobar white matter estimates for four metabolites, signal-to-noise ratio (SNR), spectral linewidth, and confidence intervals were compared to data reconstructed using no smoothing. Additionally, a voxel-wise comparison for N-acetylaspartate quantitation with different smoothing schemes was performed. ResultsSignificant pairwise differences were seen for all Gaussian smoothing methods as compared to no smoothing (p<0.001) in linewidth and metabolite estimates, whereas dnPCA methods showing no statistically significant differences in these measures. Confidence intervals decreased, and SNR increased with increasing levels of apodization smoothing or dnPCA denoising. ConclusionMild Gaussian apodization (≤2 Hz at 3 T) can be applied with minimal (1%) errors in quantitation; however, smoothing values greater than that can significantly affect metabolite quantification. In contrast, mild to moderate dnPCA based denoising provides quantitative results that are consistent with the analysis of unsmoothed data and this method is recommended for spectral denoising.

Ultrasonic signal denoising based on autoencoder.

At present, denoising parameters in different signal processing algorithms require a specific signal waveform to be set. Human factors would significantly affect the denoising result. To solve this problem, we proposed a signal adaptive denoising method based on a denoising autoencoder to achieve denoising on ultrasonic signals. By applying this method to sample signals and comparing with the singular value decomposition (SVD), principal component analysis (PCA), and wavelet algorithms, it is found that this method can effectively suppress the noise at different noise intensities. Using the signal to noise ratio, root mean square error, and autocorrelation coefficient as evaluation parameters in the experiment, the overall denoising effect of the proposed method is better than that of PCA, and this method is better than the wavelet and SVD algorithms having a relatively weak noise intensity. In addition, by comparing the reconstructed signal curve of the proposed method and that of the wavelet algorithm, the proposed method can retain the information of signal saltation with a better performance. Finally, we apply this method for processing ultrasonic signals and verify its effectiveness from time and frequency domain diagrams.

Random noise suppression and parameter estimation for Magnetic Resonance Sounding signal based on maximum likelihood estimation

Magnetic resonance sounding (MRS) is a promising geophysical method for direct detection and quantification of groundwater. However, the application of MRS is considerably limited due to its vulnerability to electromagnetic interference. In this paper, the statistical method of maximum likelihood estimation (MLE) is introduced to estimate nuclear magnetic resonance (NMR) parameters and suppress random noise in MRS. Using synthetic NMR signals and noise models, we analyse the factors that influence the denoising effect of MLE. The results show that MLE denoising can be effective even when the random noise deviates from a Gaussian distribution. A multi-exponential NMR signal may reduce the accuracy of MLE-based parameter estimation if a bad initial guess is used, but it has little effect on the suppression of random noise. Harmonic noise can reduce the effect of MLE, while impulsive noise has little effect on MLE. In addition, increasing the recording time or frequency increases the amount of data of the MRS signal, thus improving the effectiveness of the MLE method. Field examples show that this method can effectively suppress random noise interference with less stacking compared with traditional methods, thus greatly shortening the working time of MRS and improving its working efficiency.