Soft-threshold Filtering Research Articles

Fault Diagnosis Methods for an Artillery Loading System Driving Motor in Complex Noisy Environments

With the development of modern military technology, electrical drive technology has become a power source for modern artillery. In fault monitoring of a driving motor mounted on a piece of artillery, various sensors are susceptible to interference from the complex environment, both inside and outside the artillery itself. In this study, we creatively propose a fault diagnosis model based on an attention mechanism, the AdaBoost method and a wavelet noise reduction network to address the difficulty in obtaining high-quality motor signals in complex noisy interference environments. First, multiple fusion wavelet basis, soft thresholding, and index soft filter optimization were used to train multiple wavelet noise reduction networks that could recover sample signals under different noise conditions. Second, a convolutional neural network (CNN) classification module was added to construct end-to-end classification models that could correctly identify faults. The above basis classification models were then integrated into the AdaBoost method with an improved attention mechanism to develop a fault diagnosis model suitable for complex noisy environments. Finally, two experiments were conducted to validate the proposed method. Under motor signals with varying signal-to-noise ratios (SNRs) noises, the proposed method achieved an average accuracy of 92%, surpassing the conventional method by over 8.5%.

BO-MADRSN: Bayesian optimized multi-attention residual shrinkage networks for industrial soft sensor modeling

Convolutional neural networks have been widely utilized in industrial soft sensor modeling because of its excellent feature extraction capabilities in recent years. However, industrial data are often multimodal and contain noise, which affects the feature extraction capability of soft sensor models and leads to potentially large errors in the final key variable results. The solution to the above problems is based on denoising techniques and higher-level feature extraction techniques. Therefore, this paper proposes a Bayesian optimized Multi-Attention deep residual shrinkage network for industrial soft sensor model, referred to as BO-MADRSN. Firstly, in order to filter the noise in the original data during feature extraction, we used a soft thresholding method, where the thresholds are determined by back-propagation algorithm. Secondly, in order to retain the information of the data as much as possible and reduce the cases of zero after soft threshold filtering, we construct a residual network-like structure to complement the results obtained after soft threshold noise reduction by performing attentional feature extraction on the original features through the shortcut connections of this structure. Thirdly, we perform hierarchical denoising by stacking multiple residual blocks, and this structure facilitates network training. In addition, a Bayesian optimization strategy is introduced to select the optimal hyperparameters. In this paper, the results on several real industrial datasets verify the effectiveness and superiority of the proposed soft sensor model.

Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis

Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed.

Development of a novel reconstruction method for two-phase flow CT with improved simulated annealing algorithm

Two-phase flow, especially gas-liquid two-phase flow, has a wide application in industrial field. The diagnosis of two-phase flow parameters, which directly determine the flow and heat transfer characteristics, plays an important role in providing the design reference and ensuring the security of online operation of two-phase flow system. Computer tomography (CT) is a good way to diagnose such parameters with imaging method. This paper has proposed a novel image reconstruction method for thermal neutron CT of two-phase flow with improved simulated annealing (ISA) algorithm, which makes full use of the prior information of two-phase flow and the advantage of stochastic searching algorithm. The reconstruction results demonstrate that its reconstruction accuracy is much higher than that of the reconstruction algorithm based on weighted total difference minimization with soft-threshold filtering (WTDM-STF). The proposed method can also be applied to other types of two-phase flow CT modalities (such as X(γ)-ray, capacitance, resistance and ultrasound).

Texture Variation Adaptive Image Denoising With Nonlocal PCA

Image textures, as a kind of local variations, provide important information for the human visual system. Many image textures, especially the small-scale or stochastic textures, are rich in high-frequency variations, and are difficult to be preserved. Current state-of-the-art denoising algorithms typically adopt a nonlocal approach consisting of image patch grouping and group-wise denoising filtering. To achieve a better image denoising while preserving the variations in texture, we first adaptively group high correlated image patches with the same kinds of texture elements (texels) via an adaptive clustering method. This adaptive clustering method is applied in an over-clustering-and-iterative-merging approach, where its noise robustness is improved with a custom merging threshold relating to the noise level and cluster size. For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures. Experiments on natural images show the superiority of the proposed transform-domain variation adaptive filtering to traditional PCA-based hard or soft threshold filtering. As a whole, the proposed denoising method achieves a favorable texture-preserving performance both quantitatively and visually, especially for irregular textures, which is further verified in camera raw image denoising.

Improvement of detection performance on single photon lidar by EMD-based denoising method

A novel EMD-based denoising method, referred to as EMD with soft thresholding and Savitzky-Golay, is proposed for single photon lidar denoising. After the discrimination of the high-frequency and low-frequency IMFs, the soft thresholding and Savitzky-Golay filter are applied, respectively. The proposed denoising method has a better denoising performance and less signal distortion on simulated single photon lidar data than that of EMD partial reconstruction and wavelet transform method. With the proposed method, the SNR of real single photon lidar increased from 12.36 to 15.32 dB and false alarm rate is reduced obviously.

Echocardiography image denoising using fractal wavelet transform

One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is denoised using above mentioned different wavelet based denoising techniques. On comparison of the obtained results, it is observed that the fractal wavelet transform is well suited for highly degraded echocardiography images in terms of Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) than other wavelet based denoising methods. Further, the work could be enhanced to denoise the echocardiography image corrupted by other different types of noise. This research is limited to denoise the echocardiography image corrupted with Gaussian noise only.

Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction.

Background and Objective Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques. Methods First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively. Results Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method. Conclusions In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

Ordered-subset Split-Bregman algorithm for interior tomography.

Inspired by the Compressed Sensing (CS) theory, it has been proved that the interior problem of computed tomography (CT) can be accurately and stably solved if a region-of-interest (ROI) is piecewise constant or polynomial, resulting in the CS-based interior tomography. The key is to minimize the total variation (TV) of the ROI under the constraint of the truncated projections. Coincidentally, the Split-Bregman (SB) method has attracted a major attention to solve the TV minimization problem for CT image reconstruction. In this paper, we apply the SB approach to reconstruct an ROI for the CS-based interior tomography assuming a piecewise constant imaging model. Furthermore, the ordered subsets (OS) technique is used to accelerate the convergence of SB algorithm, leading to a new OS-SB algorithm for interior tomography. The conventional OS simultaneous algebraic reconstruction technique (OS-SART) and soft-threshold filtering (STF) based OS-SART are also implemented as references to evaluate the performance of the proposed OS-SB algorithm for interior tomography. Both numerical simulations and clinical applications are performed and the results confirm the advantages of the proposed OS-SB method.

Impact of view reduction in CT on radiation dose for patients

Iterative methods have become a hot topic of research in computed tomography (CT) imaging because of their capacity to resolve the reconstruction problem from a limited number of projections. This allows the reduction of radiation exposure on patients during the data acquisition. The reconstruction time and the high radiation dose imposed on patients are the two major drawbacks in CT. To solve them effectively we adapted the method for sparse linear equations and sparse least squares (LSQR) with soft threshold filtering (STF) and the fast iterative shrinkage-thresholding algorithm (FISTA) to computed tomography reconstruction. The feasibility of the proposed methods is demonstrated numerically.

A Multiresolution Bilateral Filter for Speckle Reduction in Ultrasound Kidney Images

The kidney stones (renal calculi) are the most common medical problems that occur to the people, irrespective of age group. The ultrasonography is a versatile and noninvasive imaging modality often preferred over other techniques such as CT, MRI and X-ray for kidney stone detection. It does not use any ionizing radiation for its operation. However, the main disadvantage associated with ultrasound scanning is the presence of speckle noise which mimics as a kidney stone and leads to fault diagnosis. The existence of speckle affects the further image processing steps such as feature extraction, segmentation, and classification of stones. A new wavelet based bilateral filtering technique is proposed for speckle reduction of ultrasound kidney images in order to make the diagnosis procedure of kidney stones in the easiest way. In this method, the approximation coefficients of decomposed image are processed using the bilateral filter. The noise reduction in detail coefficient is performed using wavelet soft thresholding technique. The threshold value is derived from a Bayesian framework. This method of speckle reduction is applied on various ultrasound kidney images with stone and visual investigation made on the filter performance in terms of the identification of actual stones by reducing the effect of speckle resembles like a stone. Also, the effectiveness of the proposed method is examined by means of RMSE, PSNR, and SSIM. The proposed hybrid architecture is compared with wavelet Hard and Soft thresholding and Bilateral Filter.

A soft-threshold filtering EM-TV algorithm for CT reconstruction

The expectation maximization (EM) algorithm based on statistical property can reduce image noise and artifacts in CT reconstruction. Considering its slow convergence and unsmooth of reconstruction image, this paper proposed EM-TV algorithm which is combined with the minimization of total variation (TV) and a soft-threshold filtering. To make the data smooth and do not loss the information of the edge, first, using EM algorithm reconstruct an image; then its discrete difference transform (DDT) is calculated and the soft-threshold filtering is updated. Finally, experimental results and comparisons with the EM algorithm of Shepp–Logan phantom show that the proposed algorithm can not only make the image of reconstruction smooth and reduce the normalized mean square distance, but also speed up the convergence. And real projection data indicate that this algorithm is effective.

Laser Active Image-Denoising Based on Principal Component Analysis with Local Pixel Grouping

Denoising is an important issue for laser active image. This paper attempted to process laser active image in the low-dimensional sub-space. We adopted the principal component analysis with local pixel grouping (LPG-PCA) denoising method proposed by Zhang [1], and compared it with the conventional denoising method for laser active image, such as wavelet filtering, wavelet soft threshold filtering and median filtering. Experimental results show that the image denoised by LPG-PCA has higher BIQI value than other images, most of the speckle noise can be reduced and the detail structure information is well preserved. The low-dimensional sub-space idea is a new direction for laser active image denoising.

Few-view image reconstruction with fractional-order total variation.

This work presents a novel computed tomography (CT) reconstruction method for the few-view problem based on fractional calculus. To overcome the disadvantages of the total variation minimization method, we propose a fractional-order total variation-based image reconstruction method in this paper. The presented model adopts fractional-order total variation instead of traditional total variation. Different from traditional total variation, fractional-order total variation is derived by considering more neighboring image voxels such that the corresponding weights can be adaptively determined by the model, thus suppressing the over-smoothing effect. The discretization scheme of the fractional-order model is also given. Numerical and clinical experiments demonstrate that our method achieves better performance than existing reconstruction methods, including filtered back projection (FBP), the total variation-based projections onto convex sets method (TV-POCS), and soft-threshold filtering (STH).

SART-Type Half-Threshold Filtering Approach for CT Reconstruction.

The [Formula: see text] regularization problem has been widely used to solve the sparsity constrained problems. To enhance the sparsity constraint for better imaging performance, a promising direction is to use the [Formula: see text] norm (0 < p < 1) and solve the [Formula: see text] minimization problem. Very recently, Xu et al. developed an analytic solution for the [Formula: see text] regularization via an iterative thresholding operation, which is also referred to as half-threshold filtering. In this paper, we design a simultaneous algebraic reconstruction technique (SART)-type half-threshold filtering framework to solve the computed tomography (CT) reconstruction problem. In the medical imaging filed, the discrete gradient transform (DGT) is widely used to define the sparsity. However, the DGT is noninvertible and it cannot be applied to half-threshold filtering for CT reconstruction. To demonstrate the utility of the proposed SART-type half-threshold filtering framework, an emphasis of this paper is to construct a pseudoinverse transforms for DGT. The proposed algorithms are evaluated with numerical and physical phantom data sets. Our results show that the SART-type half-threshold filtering algorithms have great potential to improve the reconstructed image quality from few and noisy projections. They are complementary to the counterparts of the state-of-the-art soft-threshold filtering and hard-threshold filtering.

基于像素点分类的激光主动成像混合滤波

In order to keep the detail information of edges while suppressing noise in laser active imaging, hybrid filter based on pixel classification is proposed. Based on contour curvature, the pixels are divided into different classes. Lee filter is used to suppress speckle noise and fuzzy weighted averaging filter is used to suppress other mixed noise. A contour point screening method is proposed to select candidate contour points from noisy image. Then the contours are thinned into single pixel. After eliminating the bogus contours brought by the noise and those that are comparatively week, the contour curvatures are calculated. According to the contour curvature, the pixels of the image are divided into different classes. For the pixels belonging to different classes, fuzzy weighted averaging filter and Lee filter with different filter parameters are used, which makes the proposed method own pixel-level adaptability. Experimental results indicate that the proposed method outperform the classical Lee, Kuan and wavelet soft-thresholding filter in denoising and keeping detail edge information.

Image reconstruction from limited angle projections collected by multisource interior x-ray imaging systems

A multisource x-ray interior imaging system with limited angle scanning is investigated to study the possibility of building an ultrafast micro-CT for dynamic small animal imaging, and two methods are employed to perform interior reconstruction from a limited number of projections collected by the multisource interior x-ray system. The first is total variation minimization with the steepest descent search (TVM-SD) and the second is total difference minimization with soft-threshold filtering (TDM-STF). Comprehensive numerical simulations and animal studies are performed to validate the associated reconstructed methods and demonstrate the feasibility and application of the proposed system configuration. The image reconstruction results show that both of the two reconstruction methods can significantly improve the image quality and the TDM-SFT is slightly superior to the TVM-SD. Finally, quantitative image analysis shows that it is possible to make an ultrafast micro-CT using a multisource interior x-ray system scheme combined with the state-of-the-art interior tomography.

Filtered wavelet thresholding methods

When working with nonlinear filtering algorithms for image denoising problems, there are two crucial aspects, namely, the choice of the thresholding parameter λ and the use of a proper filter function. Both greatly influence the quality of the resulting denoised image. In this paper we propose two new filters, which are a piecewise quadratic and an exponential function of λ, respectively, arid we show how they can be successfully used instead of the classical Donoho and Johnstone's Soft thresholding filter. We exploit the increased regularity and flexibility of the new filters to improve the quality of the final results. Moreover, we prove that our filtered approximation is a near-minimizer of the functional which has to be minimized to solve the denoising problem. We also show that the quadratic filter, due to its shape, yields good results if we choose λ as the Donoho and Johnstone universal threshold, while the exponential one is more suitable if we use the recently proposed H-curve criterion. Encouraging results in extensive numerical experiments on several test images confirm the effectiveness of our proposal.