Denoising Filter Research Articles

Research on Load Disturbance Based Variable Speed PID Control and a Novel Denoising Method Based Effect Evaluation of HST for Agricultural Machinery

This paper aims to realize and improve the constant speed control performance of tractors with HST (Hydrostatic Transmission) variable speed units. To achieve this, based on the HST test bench of the tractor, we perform a verification test of the adjustable speed characteristics, a denoising filter test of the response signal, a test on the influence of the load disturbance on the adjustable speed characteristics and a PID-based constant speed performance detection test. The results of the verification test of the adjustable speed characteristics show that the theoretical value and actual value of the adjustable speed transmission characteristics of the HST used are essentially consistent with each other. The results of the test of the load disturbance’s influence on the adjustable speed characteristics show that the increase in load torque inhibits the HST output response. Therefore, the paper proposes and designs a PID-based closed-loop constant speed control system. The paper uses a step response test and a load disturbance test to research the control result of the constant speed system. Collecting and analyzing all test results, we find that the constant speed control based on PID has a very good result. The average error between the average HST output speed and the target speed set was 0.37%, and the average standard deviation of output speed was 1.18 rpm. In addition, the paper proposes a denoising method combing the empirical mode decomposition method and the Gaussian distribution determination. The method shows that the first two orders of the components of the HST response signal should be removed as noise. The paper uses the denoised signal and the partial least squares method to analyze the influencing factors of the constant speed control result. The analysis results show that the rate of change of load torque has the biggest influence on the stability of HST output speed, followed by the target value.

Nuchal Translucency Thickness Measurement in Fetal Ultrasound Images to Analyze Down Syndrome

Down syndrome (DS) is a chromosomal condition associated with intellectual disability and developmental delay in infants. The most salient pre-natal marker to identify DS during the early stages of gestation is the Nuchal translucency (NT) thickness. Accurate NT measurement from ultrasound (US) images becomes challenging due to the presence of speckle noise, weak edges and other artifacts. This paper proposes a semi-automated approach for early identification and calibration of NT thickness by estimating the distance between the edges of NT. A comparative study is performed on six traditional filters to determine the effective denoising filter for US datasets. This paper also presents an evaluation of two popular segmentation algorithms, the Active Contour and Chan-Vese-based segmentation. The study reveals that Wiener filter outperformed other filters based on the performance metrics and achieved peak signal noise ratio of 42.88 dB and mean squared error of −3.45. The prime feature NT region has been segmented effectively using enhanced double region-based active contour segmentation and obtained Dice Similarity Index (DSI- 84%) and Jaccard Index (76%). This technique has been validated by using Bland Altman’s plot to determine the correlation between clinical and proposed method.

Application of Two New Feature Fusion Networks to Improve Real-time Prostate Capsula Detection.

Excess prostate tissue is trimmed near the prostate capsula boundary during transurethral plasma kinetic enucleation of prostate (PKEP) and transurethral bipolar plasmakinetic resection of prostate (PKRP) surgeries. If a large portion of the tissue is removed, a prostate capsula perforation can potentially occur. As such, real-time accurate prostate capsula (PC) detection is critical for the prevention of these perforations. This study investigated the potential for using image denoising, image dimension reduction and feature fusion to improve real-time prostate capsula detection with two objectives. First, this paper mainly studied feature selection and input dimension reduction. Secondly, image denoising was evaluated, as it is of paramount importance to transient stability assessment based on neural networks. Two new feature fusion techniques, maxpooling bilinear interpolation single-shot multibox detector (PBSSD) and bilinear interpolation single shot multibox detector (BSSD) were proposed. Before original images were sent to the neural network, they were processed by principal component analysis (PCA) and adaptive median filter (AMF) for dimension reduction and image denoising. The results showed that the application of PCA and AMF with PBSSD increased the mean average precision (mAP) for prostate capsula images by 8.55% and reached 80.15%, compared with single shot multibox detector (SSD) alone. Application of PCA with BSSD increased the mAP for prostate capsula images by 4.6% compared with SSD alone. Compared with other methods, ours were proven to be more accurate for real-time prostate capsula detection. The improved mAP results suggest that the proposed approaches are powerful tools for improving SSD networks.

Experiment on a distributed fiber optic interferometric sensing system to monitor and locate urban high-density polyethylene gas pipe leakage

A distributed fiber optical interferometric sensing system was built to monitor and detect urban high-density polyethylene gas pipe leakage. To eliminate the phase drift, the digital phase generated carrier scheme was designed to demodulate the phase difference of interferometers induced by high-density polyethylene gas pipe leakage. Then the empirical mode decomposition and approximate entropy were combined to process fiber optic interferometric signals. Finally, leakage points were located by the null-frequency spectrum after empirical mode decomposition filter denoising. The lab-scale test validated the distributed fiber optical interferometric sensing system leakage location performance and demonstrated a reduced leakage accident missed alarm rate.

Measurement accuracy enhancement with multi-event detection using the deep learning approach in Raman distributed temperature sensors.

In this work, we present a novel deep learning framework for multi-event detection with enhanced measurement accuracy from the measured data of a Raman Optical Time Domain Reflectometer (Raman-OTDR). We demonstrate the utility of a deep learning-based approach by comparing the results from three popular neural networks, i.e. vanilla recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Before feeding the experimentally obtained data to the neural network, we sanitize our data through a correlation filtering operation to suppress outlier noise spikes. Based on experiments with Raman-OTDR traces consisting of single temperature event, we show that the GRU is able to provide better performance compared to RNN and LSTM models. Specifically, a bidirectional-GRU (bi-GRU) architecture is found to outperform other architectures owing to its use of data from both previous as well as later time steps. Although this feature is similar to that used recently in one dimension convolutional neural network (1D-CNN), the bi-GRU is found to be more effective in providing enhanced measurement accuracy while maintaining good spatial resolution. We also propose and demonstrate a threshold-based algorithm for accurate and fast estimation of multiple events. We demonstrate a 4x improvement in the spatial resolution compared to post-processing using conventional total variational denoising (TVD) filters, while the temperature accuracy is maintained within ± 0.5 oC of the set temperature.

An adaptive patch-based goldstein filter for interferometric phase denoising

ABSTRACT The Goldstein filter was a well-known approach of synthetic aperture radar interferogram filtering for its simplicity and high efficiency. The filtering performance of Goldstein and its improved filters is mostly affected by several parameters, such as the noise suppression parameter α, the sliding patch size, and the kernel window size. Yet, the fixed parameters will cause the filtering intensity to mismatch the noise levels of different regions. In this paper, we propose an adaptive patch-based Goldstein filter (AP-Goldstein filter), which takes the pseudo-variation coefficient as the measure of patch homogeneity and adaptively selects the patch sizes in different regions of interferogram. This filter controls the noise suppression parameter α by pseudo-coherence, which is a common and effective indicator applied to filter the interferogram phase. Meanwhile, the previously determined patch size of each pixel is used to confirm the kernel window size at the same region. The proposed and other advanced filters were tested on real and simulated interferograms. The qualitative and quantitative comparison of the experimental results shows that this new method can better restrain the phase noise of interferogram while maintaining the edge detailed information and can improve the automation of filtering.

Automated Method for Solid State Nuclear Track Measurements

Solid state nuclear track detection technology is an important nuclear technology based on the measurement of track density and geometric parameters, but manual measurement is difficult to ensure the accuracy and efficiency. Aiming at the uneven brightness, low contrast and noise pollution of nuclear track images, this paper proposes a method of acquisition, brightness correction, de-noising filter, target segmentation, boundary extraction, recognition of solid nuclear track image, and finally obtaining the geometric characteristic parameters of nuclear track, which improves the accuracy and efficiency of nuclear track parameter measurement.

GPR noise reduction by TVD and TVD-EMD

The existence of coherent and incoherent (random) noises in the ground penetration radar (GPR) signals using high-frequency electromagnetic waves is inevitable; therefore, the elimination of noise from GPR data before performing any additional analysis is of great importance to increase the efficiency of the interpretations. We apply the Total Variation De-noising (TVD) and Savitzky-Golay (SG) filter, on the synthetic and real GPR data. For a better perception, we compare the same trace of the data before and after the experiences. The results indicate that the TVD method is more effective than common adaptive filtering in the time domain for reducing noise such as the SG filter which acts as a low-pass filter for smoothing data based on a polynomial least-squares approximation; however, due to the visibility of staircase artifacts using the TVD method, GPR data is first transferred to the empirical mode decomposition (EMD) frame which is useful for non-linear and non-stationary signal processing, then the TVD method is applied on it; ultimately, noise reduction using TVD is compared in the time and EMD domains. The comparison of the outputs shows that the TVD algorithm in the EMD domain, based on the sequential extraction of the energy belonging to the different intrinsic time scales of the signal, provides better noise attenuation than the other algorithms. In addition, TVD-EMD preserves the event forms, signal forms and improves the continuity in sections.

PPP-BOTDA Distributed Optical Fiber Sensing Technology and Its Application to the Baishuihe Landslide

Serious landslide hazards are prevalent along the Yangtze River in China, particularly in the Three Gorges Reservoir area. Thus, landslide monitoring and forecasting technology research is critical if landslide geological hazards are to be prevented and controlled. Pulse-prepump brillouin optical time domain analysis (PPP-BOTDA) distributed optical fiber sensing technology is a recently developed monitoring method with evident advantages in precision and spatial resolution. Herein, fixed-point immobilization and direct burying methods were adopted to arrange parallel distribution of the strain and temperature-compensated optical fibers along the Baishuihe landslide’s front edge, in order to carry out ground surface deformation monitoring. The strain data acquired from both optical fibers were processed with temperature compensation to obtain the actual optical fiber strain produced by deformation. Butterworth low-pass filter denoising method was employed to determine the filter order (n) and cut-off frequency (Wn). The area differences between the two optical fiber monitoring curves and the fixed horizontal axis were selected as evaluation indexes to obtain the area difference along the optical fiber. This data were then leveraged to determine the positive correlation between the area difference and the optical fiber strain variation degree. Finally, these results were compared with the GPS and field measured data. This study shows that when PPP-BOTDA technology is used for landslide surface deformation monitoring in conjunction with Butterworth filter denoising and strain area difference, the optical fiber strain variation degree analysis results are consistent with the GPS monitoring data and the actual landslide deformation. As such, this methodology is highly relevant for reducing the workload and improving the monitoring precision in landslide monitoring, which in turn will protect lives and property.

Robust segmentation method for noisy images based on an unsupervised denosing filter

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the noisy image input. Finally, the level-set energy function is minimized to acquire segmentation contours. Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.

Image Denoising of Adaptive Fractional Operator Based on Atangana–Baleanu Derivatives

A fractional integral operator can preserve an image edge and texture details as a denoising filter. Recently, a newly defined fractional-order integral, Atangana–Baleanu derivatives (ABC), has been used successfully in image denoising. However, determining the appropriate order requires numerous experiments, and different image regions using the same order may cause too much smoothing or insufficient denoising. Thus, we propose an adaptive fractional integral operator based on the Atangana–Baleanu derivatives. Edge intensity, global entropy, local entropy, and local variance weights are used to construct an adaptive order function that can adapt to changes in different regions of an image. Then, we use the adaptive order function to improve the masks based on the Grumwald–Letnikov scheme (GL_ABC) and Toufik–Atangana scheme (TA_ABC), namely, Ada_GL_ABC and Ada_TA_ABC, respectively. Finally, multiple evaluation indicators are used to assess the proposed masks. The experimental results demonstrate that the proposed adaptive operator can better preserve texture details when denoising than other similar operators. Furthermore, the image processed by the Ada_TA_ABC operator has less noise and more detail, which means the proposed adaptive function has universality.

Evaluation of the effect of filters on reconstructed image quality from cone beam CT system

: 3D Filtered Back Projection (FBP) is a three-dimensional reconstruction algorithm usually used in Cone Beam Computed Tomography (CBCT) system. FBP is one of the most popular algorithms due to its reconstruction is fast while quality of the result is acceptable. It can also handle a more considerable amount of data with same computer performance with other algorithms. However, the quality of a reconstructed image by the FBP algorithm strongly depends on spatial filters and denoising filters applied to projections. In this paper an evaluation of the reconstructed image quality of the CBCT system by using different denoising filters and spatial filters to find out the best filters for the CBCT system is performed. The result shows that, there is a significantly decrease of the noise of projection with the combination of Median and Gaussian filters. The reconstructed image has high resolution with Cosine filter and becomes more sharpen with Hanning filter.

Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach

Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.

GPU Accelerated Bilateral Filter for MR Image Denoising

Background: Magnetic resonance (MR) imaging plays a significant role in the computer- aided diagnostic systems for remote healthcare. In such systems, the soft textures and tissues within the denoised MR image are classified by the segmentation stage using machine learning algorithms like Hidden Markov Model. Thus, the quality of the MR image is of extreme importance and is decisive in the accuracy of the process of classification and diagnosis. Objective: To provide real-time medical diagnostics in the remote healthcare intelligent setups, the research work proposes CUDA GPU based accelerated bilateral filter for fast denoising of 2D high- resolution knee MR images. Methods: To achieve optimized GPU performance with better speed-up, the work implements an improvised technique that uses on-chip shared memory in combination with a constant cache. Results: The speed-up of 382x is achieved with the new proposed optimization technique which is 2.7x as that obtained with the shared memory only approach. The superior speed-up is along with 90.6%occupancy index indicating effective parallelization. The work here also aims at justifying the appropriateness of bilateral filter over other filters for denoising magnetic resonance images. All the patents related to GPU based image denoising are revised and uniqueness of the proposed technique is confirmed. Conclusion: The results indicate that even for a 64Mpixel image, the execution time of the proposed implementation is 334.91 msec only, making the performance almost real time. This will surely contribute to the real-time computer-aided data diagnostics requirement under remote critical conditions.

Automatic Segmentation and Detection System for Varicocele in Supine Position

Image analysis is an important technique that can help specialists localize, detect, and segment objects in different types of medical images such as MRI, CTs, and ultrasounds (US). In this research, we use US images to identify and segment the enlarged veins in the pampiniform venous plexus, which is called varicocele. The proposed method aims to determine whether a potential patient is affected or not. This method was evaluated using 90 US images that were taken of the left testicles of 90 patients in the Supine position. This system analyzes US images in three stages which are; preprocessing, processing, and edge detection. The Region Of Interest (ROI) of the pampiniform plexus area was extracted using Otsu segmentation with different parameters (0.1, 0.2, 0.17) and different color modes (Grayscale, YCbCr, RGB). In the processing stage, different denoising filters were used. Eventually, in the edge detection stage, four edge detectors were applied which are Canny, Soble, Prewitt, and Roberts. Results showed that the best accuracy in detecting varicocele was 78% when the YCbCr color mode yellow (y) channel was used with 0.1 Otsu segmentation and the Canny edge detector. The system also showed a Sensitivity of 91%, as the test was able to detect 91% of the people with Varicocele, and the Specificity value was 39%.

Smooth pulse recovery based on hybrid wavelet threshold denoising and first derivative adaptive smoothing filter

Abstract Based on the pulse-shaping unit in the front end of high-power laser facilities, we propose a new hybrid scheme in a closed-loop control system including wavelet threshold denoising for pretreatment and a first derivative adaptive smoothing filter for smooth pulse recovery, so as to effectively restrain the influence of electrical noise and FM-to-AM modulation in the time–power curve, and enhance the calibration accuracy of the pulse shape in the feedback control system. The related simulation and experiment results show that the proposed scheme can obtain a better shaping effect on the high-contrast temporal shape in comparison with the cumulative average algorithm and orthogonal matching pursuit algorithm combined with a traditional smoothing filter. The implementation of the hybrid scheme mechanism increased the signal-to-noise ratio of the laser pulse from about 11 dB to 30 dB, and the filtered pulse is smooth without modulation, with smoothness of about 98.8%.

Multiscale Optimal Filtering on the Sphere

We present a framework for the optimal filtering of spherical signals contaminated by realizations of an additive, zero-mean, uncorrelated and anisotropic noise process on the sphere. Filtering is performed in the wavelet domain given by the scale-discretized wavelet transform on the sphere. The proposed filter is optimal in the sense that it minimizes the mean square error between the filtered wavelet representation and wavelet representation of the noise-free signal. We also present a simplified formulation of the filter for the case when azimuthally symmetric wavelet functions are used. We demonstrate the use of the proposed optimal filter for denoising of an Earth topography map in the presence of additive, zero-mean, uncorrelated and white Gaussian noise, and show that the proposed filter performs better than the hard thresholding method and weighted spherical harmonic (weighted-SPHARM) signal estimation framework.

Blind Image Source Camera Identification

Advanced imaging devices that are effective and widely available make it easier to create and control digital photographs. As a consequence it is not easier to validate the authenticity of digital images in the case of legal issues. The proposed method is an effective source examination and identification procedure for an image whose source camera is being investigated, with no prior knowledge of either the source camera or the picture. Even though Photo Response Non-Uniformity (PRNU) has acquired an incredible interest in image forensics the extraction of camera fingerprint with minimum seen content is still a challenging problem. In the proposed method fingerprint is extracted using a weighted combination of two types of de-noising filters, weighted nuclear norm minimization (WNNM) based filter and wavelet filter. A set of images captured by different mobile cameras at different locations are used as the dataset. Each image is distinct and contains different seen details having varying exposures. After the fingerprint extraction process, the fingerprints are clustered using correlation clustering followed by consensus clustering and final refinement based on their origin.

A real time adaptive multiresolution adaptive Wiener filter based on adaptive neuro-fuzzy inference system and fuzzy evaluation

In this paper, a real-time denoising filter based on modelling of stable hybrid models is presented. The hybrid models are composed of the shearlet filter and the adaptive Wiener filter in different forms. The optimization of various models is accomplished by the genetic algorithm. Next, regarding the significant relationship between Optimal models and input images, changing the structure of Optimal models for image denoising is modelled by the ANFIS. The eight hundred digital images are used as train images. For eight hundred training images, Sixty seven models are found. For integrated evaluation, the amounts of image attributes such as Peak Signal to Noise Ratio, Signal to Noise Ratio, Structural Similarity Index, Mean Absolute Error and Image Quality Assessment are evaluated by the Fuzzy deduction system. Finally, for the features of a sample noisy image as test data, the proposed denoising model of ANFIS is compared with wavelet filter in 2 and 4 level , Fast bilateral filter, TV-L1, Median, shearlet filter and the adaptive Wiener filter. In addition, run time of proposed method are evaluated. Experiments show that the proposed method has better performance than others.

Survey on mixed impulse and Gaussian denoising filters

This study presents a comprehensive survey on mixed impulse and Gaussian denoising filters which are applied to an image in order to gauge the effects of this type of noise combination and to then determine optimal ways that can overcome such effects. The random noise model considered in this survey is the combined effect of impulse (salt and pepper) and Gaussian noise. After describing the noise models, the denoising filters which are applied to the images are classified and explained according to their design structure, the type of filters they use, the noise level they could overcome, and the limitations they face. This survey covers all related denoising methods and provides an assessment of the strengths and practical limitations of the different classes of denoising filters.