De-noising Algorithm Research Articles

Low-Rank Matrix Denoising Algorithm-Based Magnetic Resonance Imaging Combined with Computed Tomography Images in the Diagnosis of Cerebral Aneurysm

This study was to analyze the diagnostic effects of computed tomography (CT) and magnetic resonance imaging (MRI) in patients with cerebrovascular diseases (CVDs) based on low-rank matrix denoising (LRMD) algorithm. The LRMD algorithm was adopted for MRI diagnosis and CT diagnosis for comparative analysis. 129 CVD patients were selected as the research objects, 43 cases were diagnosed by CT, 43 cases were diagnosed by MRI under LRMD, and the other 43 cases were diagnosed by CT + MRI. The results showed that the diagnostic compliance rates (DCRs) of CT group in the cerebral hemorrhage (CH), cerebral infarction (CI), and cerebral aneurysm (CA) were 95.1%, 94.7%, and 70%, respectively, while those in the MRI group were 99.01%, 97.71%, and 100%, respectively. Thus, it was obtained that MRI diagnosis was much better than CT diagnosis, and CT + MRI showed the best diagnosis efficacy, showing statistical differences ( P < 0.05 ). The accuracy, sensitivity, and specificity of MRI diagnosis under the LRMD algorithm were 96.28%, 88.76%, and 90.62%, respectively, which were superior to those of CT diagnosis (92.71%, 84.94%, and 80.71%, respectively). The diagnosis cost per case (DC/C) (799.73 ± 100.02 yuan) and the total diagnosis cost (TDC) (58,521.67 ± 301.62 yuan) in the MRI group were higher than those in the CT group (601.42 ± 83.61 yuan and 39,819.2 ± 198.72, respectively) ( P < 0.05 ). In conclusion, CT + MRI under the LRMD algorithm showed good potential in diagnosis of CVD; MRI based on the LRMD algorithm showed a higher positive rate in the diagnosis of CA and was better than CT diagnosis, and CT + MRI showed the best diagnosis effect and could improve the clinical diagnosis rate.

Image Noise Removal Method Based on Improved Nonlocal Mean Algorithm

Aiming at the problem of unclear images acquired in interactive systems, an improved image processing algorithm for nonlocal mean denoising is proposed. This algorithm combines the adaptive median filter algorithm with the traditional nonlocal mean algorithm, first adjusts the image window adaptively, selects the corresponding pixel weight, and then denoises the image, which can have a good filtering effect on the mixed noise. The experimental results show that, compared with the traditional nonlocal mean algorithm, the algorithm proposed in this paper has better results in the visual quality and peak signal-to-noise ratio (PSNR) of complex noise images.

Covid-19 Images and Video Denoising Algorithms Based on Convolutional Neural Network CNNs

In this paper, the most sophisticated denoising algorithms of images and video are applied and implemented. More precisely, we study and implement the video denoising algorithms "VBM3D", "VBM4D", "DVDNet" and "FastDVDnet". Much attention is given to the latest DVDNet and FastDVDNet algorithms, which are based on CNN. We carry out a detailed quantitative and qualitative comparative study between the considered algorithms. Two assessments are adapted; the first is a qualitative comparison based on the qualityof the images / videos and the second is quantitative in terms of PSNR and running time criteria. To see the direct impact of our study on the current pandemic, and to show the importance of image and video preprocessing algorithms in the field of medical imaging; we apply the considered denoising algorithms based on CNN on our built COVID- 19 dataset and TEST_PCR videos.

Fast Cadzow’s Algorithm and a Gradient Variant

The Cadzow’s algorithm is a signal denoising and recovery method which was designed for signals corresponding to low rank Hankel matrices. In this paper we first introduce a Fast Cadzow’s algorithm which is developed by incorporating a novel subspace projection to reduce the high computational cost of the SVD in the Cadzow’s algorithm. Then a Gradient method and a Fast Gradient method are proposed to address the non-decreasing MSE issue when applying the Cadzow’s or Fast Cadzow’s algorithm for signal denoising. Extensive empirical performance comparisons demonstrate that the proposed algorithms can complete the denoising and recovery tasks more efficiently and effectively.

Free-Field Cortical Steady-State Evoked Potentials in Cochlear Implant Users

Auditory steady-state evoked potentials (SS-EPs) are phase-locked neural responses to periodic stimuli, believed to reflect specific neural generators. As an objective measure, steady-state responses have been used in different clinical settings, including measuring hearing thresholds of normal and hearing-impaired subjects. Recent studies are in favor of recording these responses as a part of the cochlear implant (CI) device-fitting procedure. Considering these potential benefits, the goals of the present study were to assess the feasibility of recording free-field SS-EPs in CI users and to compare their characteristics between CI users and controls. By taking advantage of a recently developed dual-frequency tagging method, we attempted to record subcortical and cortical SS-EPs from adult CI users and controls and measured reliable subcortical and cortical SS-EPs in the control group. Independent component analysis (ICA) was used to remove CI stimulation artifacts, yet subcortical responses of several CIs were heavily contaminated by these artifacts. Consequently, only cortical SS-EPs were compared between groups, which were found to be larger in the controls. The lower cortical SS-EPs' amplitude in CI users might indicate a reduction in neural synchrony evoked by the modulation rate of the auditory input across different neural assemblies in the auditory pathway. The brain topographies of cortical auditory SS-EPs, the time course of cortical responses, and the reconstructed cortical maps were highly similar between groups, confirming their neural origin and possibility to obtain such responses also in CI recipients. As for subcortical SS-EPs, our results highlight a need for sophisticated denoising algorithms to pinpoint and remove artifactual components from the biological response.

Optical coherence tomographic image denoising based on Chi-square similarity and fuzzy logic

Optical coherence tomography (OCT) is a high-resolution optical imaging technology that has been widely used in various fields, such as medical diagnostics, biological systems and materials science. However, because of the low-coherence interference of light, OCT images are inevitably destroyed by speckle noise. To remove speckle noise, an iterative contraction algorithm based on Chi-square similarity and fuzzy logic is proposed in this paper. An OCT image is first divided into a lot of overlapping image blocks, and a Chi-square distance similar block matching is utilized to form a low rank group matrix. Then, the singular value decomposition of the group matrix is performed, and the singular values are contracted by different weights with fuzzy logic. Finally, a pixel intensity fuzzy classification backward projection technique and an adaptive iterative stopping strategy are used to enhance the denoising effect. Extensive experiments are performed on 18 OCT images of the human retina. Compared with several state-of-the-art denoising algorithms, the experimental results show that the proposed algorithm obtains better objective indicators and visual inspection.

Improved complete ensemble empirical mode decomposition with adaptive noise: quasi-oppositional Jaya hybrid algorithm for ECG denoising

Improved complete ensemble empirical mode decomposition with adaptive noise: quasi-oppositional Jaya hybrid algorithm for ECG denoising

Underwater Acoustic Communication Receiver Using Deep Belief Network

Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique–Deep Belief Network (DBN) – to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre-processed using a novel pixelization algorithm. Then, using the DBN based de-noising algorithm, features are extracted from these frames and used to reconstruct the received signal. Finally, DBN based classification of the reconstructed signal occurs. Our proposed DBN based receiver system does show better performance in channels influenced by the Doppler effect and multi-path propagation with a performance improvement of 13.2dB at 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> Bit Error Rate (BER).

Noise reduction for ground-based atmospheric detection lidar: A universal method based on signal segmentation and reconstruction

Atmospheric detection lidar is a powerful tool to achieve accurate detection of various elements of the atmosphere. However, noise is an unavoidable factor in the lidar signal, and it affects the analysis of the detection results. In this study, a universal de-noising algorithm based on signal segmentation and reconstruction is proposed to enhance the signal-to-noise ratio for ground-based atmospheric detection lidar. The proposed algorithm performs well in most cases The signal segmentation, which serves as the keystone of this universal algorithm, segments the signal into three parts: the noise-free signal, normal signal, and layer signal (if it exists). The two latter signal fragments must be processed by a de-noising method, which is a combination of the empirical mode decomposition (EMD) and the discrete wavelet transform (DWT) with a fixed wavelet base. The former one deals with the normal signal, and the latter one is designed to process the layer signal. The determination of the starting and ending bins of each signal fragment, the self-adaptive EMD, and a proper selection of DWT ensure that signal processing can be accomplished automatically without any artificial intervention. A series of simulation tests quantitatively shows the advantages of the proposed method. The dual field-of-view Mie lidar provides the possibility of de-noising evaluation based on real lidar data. Our method is proven as a universal and effective approach for de-noising processing of lidar signal.

Optimized Double-Regional Filtering Algorithm on MRI Three-Dimensional Reconstructed Images for the Evaluation of Effects of Delivery on the Pelvis of Primiparas

This study was to explore the denoising and segmentation effect of dual-domain image denoising (DDID) algorithm, and the Galois field (GF) and nonlocal means (NLM) algorithms were introduced for comparative analysis. 40 primiparas in the hospital from January 2018 to January 2020 were divided into an experimental group (caesarean section (CS), group E) and a control group (vaginal delivery (VD), group C). The peak signal-to-noise ratio (PSNR) and segmentation parameters of DDID algorithm were compared with GF algorithm and NLM algorithm. It was found that the DDID showed higher overall accuracy (OA) and lower false positive rate (FPR) and false negative rate (FNR). The PSNR of DDID was higher than the other two algorithms. GF algorithm showed the highest edge retention index (ERI). The incidence of pelvic organ prolapse (POP) in group E and group C was 9/20 (45%) and 5/20 (25%), respectively, with extreme difference ( P &lt; 0.05 ). Evaluation of the effects of delivery on the pelvis of primiparas with MRI three-dimensional (3D) reconstructed images based on the optimized DDID showed a superior and stable denoising effect and good segmentation, so it was worthy of clinical promotion and application.

Development of a Smartphone-Based Optical Device to Measure Hemoglobin Concentration Changes for Remote Monitoring of Wounds.

Telemedicine (TM) can revolutionize the impact of diabetic wound care management, along with tools for remote patient monitoring (RPM). There are no low-cost mobile RPM devices for TM technology to provide comprehensive (visual and physiological) clinical assessments. Here, a novel low-cost smartphone-based optical imaging device has been developed to provide physiological measurements of tissues in terms of hemoglobin concentration maps. The device (SmartPhone Oxygenation Tool—SPOT) constitutes an add-on optical module, a smartphone, and a custom app to automate data acquisition while syncing a multi-wavelength near-infrared light-emitting diode (LED) light source (690, 810, 830 nm). The optimal imaging conditions of the SPOT device were determined from signal-to-noise maps. A standard vascular occlusion test was performed in three control subjects to observe changes in hemoglobin concentration maps between rest, occlusion, and release time points on the dorsal of the hand. Hemoglobin concentration maps were compared with and without applying an image de-noising algorithm, single value decomposition. Statistical analysis demonstrated that the hemoglobin concentrations changed significantly across the three-time stamps. Ongoing efforts are in imaging diabetic foot ulcers using the SPOT device to assess its potential as a smart health device for physiological monitoring of wounds remotely.

Performance Improvement in Electroencephalogram Signal by Using DWT

The nature of the electroencephalogram (EEG) signal is very fluid and spontaneous. Because of its lower amplitude, it is polluted by certain artifacts such as power line noise and baseline noise. Similarly, power line and baseline sounds have polluted electromyogram (EMG) and electrocardiogram (ECG) signals. These objects taint the initial signal's properties. Owing to the existence of objects, these signals cannot be accurately analyzed; hence, these sounds must be removed before analyzing the raw signal. The two important artifacts that corrupt a patient's EEG record are power line interference and baseline interference. This paper focuses on extracting power line and baseline artifacts from EEG signals using an effective de-noising algorithm called DWT (Discrete Wavelet Transform) to improve the signal's efficiency by increasing fidelity parameters including MSE, MAE, SNR, and PSNR. The MATLAB simulation is to be used for the implementation.

Improved Light Field Compression Efficiency through BM3D-Based Denoising Using Inter-View Correlation.

Multi-view or light field images have recently gained much attraction from academic and commercial fields to create breakthroughs that go beyond simple video-watching experiences. Immersive virtual reality is an important example. High image quality is essential in systems with a near-eye display device. The compression efficiency is also critical because a large amount of multi-view data needs to be stored and transferred. However, noise can be easily generated during image capturing, and these noisy images severely deteriorate both the quality of experience and the compression efficiency. Therefore, denoising is a prerequisite to produce multi-view-based image contents. In this paper, the structural characteristics of linear multi-view images are fully utilized to increase the denoising speed and performance as well as to improve the compression efficiency. Assuming the sequential processes of denoising and compression, multi-view geometry-based denoising is performed keeping the temporal correlation among views. Experimental results show the proposed scheme significantly improves the compression efficiency of denoised views up to 76.05%, maintaining good denoising quality compared to the popular conventional denoise algorithms.

Improved nerve conspicuity with water-weighting and denoising in two-point Dixon magnetic resonance neurography

BackgroundT2-weighted, two-point Dixon fast-spin-echo (FSE) is an effective technique for magnetic resonance neurography (MRN) that can provide quantitative assessment of muscle denervation. Low signal-to-noise ratio and inadequate fat suppression, however, can impede accurate interpretation. PurposeTo quantify effects of principal component analysis (PCA) denoising on tissue signal intensities and fat fraction (FF) and to determine qualitative image quality improvements from both denoising and water-weighting (WW) algorithms to improve nerve conspicuity and fat suppression. Study typeProspective. SubjectsTwenty-one subjects undergoing MR neurography evaluation (11/10 male/female, mean age = 46.3±13.7 years) with 60 image volumes. Twelve subjects (23 image volumes) were determined to have muscle denervation based on diffusely elevated T2 signal intensity. Field strength/sequence3 T, 2D, two-point Dixon FSE. AssessmentQualitative assessment included overall image quality, nerve conspicuity, fat suppression, pulsation and ringing artifacts by 3 radiologists separately on a three-point scale (1 = poor, 2 = average, 3 = excellent). Quantitative measurements for FF and signal intensity relative to normal muscle were made for nerve, abnormal muscle and subcutaneous fat. Statistical testsLinear and ordinal regression models were used for quantitative and qualitative comparisons, respectively; 95% confidence intervals (CIs) and p-values for pairwise comparisons were adjusted using the Holm-Bonferroni method. Inter-rater agreement was assessed using Gwet's agreement coefficient (AC2). ResultsSimulations showed PCA-denoising reduced FF error from 2.0% to 1.0%, and from 7.6% to 3.1% at noise levels of 10% and 30%, respectively. In human subjects, PCA-denoising did not change signal levels and FF quantitatively. WW decreased fat signal significantly (−83.6%, p < 0.001). Nerve conspicuity was improved by WW (odds ratio, OR = 5.8, p < 0.001). Fat suppression was improved by both PCA (OR = 3.6, p < 0.001) and WW (OR = 2.2, p < 0.001). Overall image quality was improved by PCA + WW (OR = 1.7, p = 0.04). ConclusionsWW and PCA-denoising improved nerve conspicuity and fat suppression in MR neurography. Denoising can potentially provide improved accuracy of FF maps for assessing fat-infiltrated muscle.

Performance evaluation of adaptive neuro fuzzy system (ANFIS) over fuzzy inference system (FIS) with optimization algorithm in de-noising of images from salt and pepper noise

In all image processing task image pre-processing is very important. It includes image enhancement and image de-noising. It is the process of removing unwanted information from an image. Image de-noising plays very important role in all image processing based research. An adaptive neural network (ANN) plays a vital role in many research areas such as image processing, character recognition, forecasting and hand writing recognition etc. An ANN is very much useful in machine learning and problem solving. Now a day, neuro fuzzy system is famous in research because it is easy enough in handling the nature of complex problems and imprecise data. The main objective of the paper is removing impulse noise from the image using adaptive neuro fuzzy inference system (ANFIS). It combines the learning abilities of neural network with inference capability of fuzzy system. Membership function plays vital role in fuzzy system. Different types of membership function are there, among which for our research we have used the triangular membership function. The proposed network is used to find different noisy pixel patterns. The fuzzy techniques with the network filter the noise without affecting the textures and fine details of the image. From the experimental results it is concluded that ANFIS perform better than fuzzy inference system (FIS) with optimization algorithm. The PSNR value is used as a performance metric. The proposed method is evaluated on standard test images. The result of ANFIS system is compared with other noise restoration methods such as mean filter, median filter, fuzzy system and fuzzy system with optimization algorithm. The experimental result shows that our proposed method achieves the maximum PSNR of 62.510 db which is high when compared with other presented methods.

Sound singularity analysis for milling tool condition monitoring towards sustainable manufacturing

Manufacturing plays an important role since they are among the largest energy consumers in modern societies. With the enhancement of environmental protection and a severe shortage of energy and resources globally, sustainable manufacturing technology has been recognized as an important future trend of manufacturing industries. Tool wear is inevitable in manufacturing and affects the surface quality and geometric tolerance significantly. Therefore, a robust and efficient Tool Condition Monitoring (TCM) system is needed to maximize tool life, ensure work-piece quality and benefit the cost control of manufacturers. Even though lots of tool condition monitoring systems have been established using various sensors, there is still an urgent demand for a low-cost and simple setup system. This article presents a sound singularity analysis approach for TCM in milling; this approach has never been previously employed for milling tools. A de-noising algorithm based on Wavelet Transform Modulus Maxima (WTMM) estimation was proposed to eliminate noises and preserve singularities. Then a wavelet basis selection method was established for optimal sound singularity analysis. The singularity was estimated by Holder Exponents (HE). A full tool life-cycle milling experiments were conducted to obtain the sound signals. The mutual information method was employed to rank HE features. The Means, Standard deviations, Minima of estimated HEs and Quantities of singular points are found most correlated to tool conditions. Then a Support Vector Machine (SVM) model trained by these features for TCM has been proposed, achieving classification accuracy of 85%. Finally, the manufacturing sustainability of the TCM approach was evaluated by tracking and improving the usages of ten same type cutters in the CNC manufacturing plant. Experimental results indicate that this approach is efficient and capable of providing effective guidance on tool replacement, and can enhance the manufacturing sustainability.

Clinical Utility of Artificial Intelligence Algorithms to Enhance Wide-Field Optical Coherence Tomography Angiography Images.

The aim of this paper is to investigate the clinical utility of the application of deep learning denoise algorithms on standard wide-field Optical Coherence Tomography Angiography (OCT-A) images. This was a retrospective case-series assessing forty-nine 10 × 10 mm OCT-A1 macula scans of 49 consecutive patients attending a medical retina clinic over a 6-month period. Thirty-seven patients had pathology; 13 had none. Retinal vascular layers were categorised into superficial or deep capillary plexus. For each category, the retinal experts compared the original standard image with the same image that had intelligent denoise applied. When analysing the Superficial Capillary Plexus (SCP), the denoised image was selected as “best for clinical assessment” in 98% of comparisons. No difference was established in the remaining 2%. On evaluating the Deep Capillary Plexus (DCP), the denoised image was preferred in 35% of comparisons. No difference was found in 65%. There was no evidence of new artefactual features nor loss of anatomical detail in denoised compared to the standard images. The wide-field denoise feature of the Canon Xephilio OCT-A1 produced scans that were clinically preferable over their original OCT-A images, especially for SCP assessment, without evidence for causing a new artefactual error.

Nonconvex Energy Minimization with Unsupervised Line Process Classifier for Efficient Piecewise Constant Signals Reconstruction

In this paper, we focus on the problem of signal smoothing and step-detection for piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis, and anomaly detection in genetics. We present a two-stage approach to minimize the well-known line process model which arises from the probabilistic representation of the signal and its segmentation. In the first stage, we minimize a TV least square problem to detect the majority of the continuous edges. In the second stage, we apply a combinatorial algorithm to filter all false jumps introduced by the TV solution. The performances of the proposed method were tested on several synthetic examples. In comparison to recent step-preserving denoising algorithms, the acceleration presents a superior speed and competitive step-detection quality.

Relationships among Fecal, Air, Oral, and Tracheal Microbial Communities in Pigs in a Respiratory Infection Disease Model.

The association of the lower respiratory tract microbiome in pigs with that of other tissues and environment is still unclear. This study aimed to describe the microbiome of tracheal and oral fluids, air, and feces in the late stage of Mycoplasma hyopneumoniae infection in pigs, and assess the association between the tracheal microbiome and those from air, feces, and oral fluids. Tracheal fluids (n = 73), feces (n = 71), oropharyngeal fluids (n = 8), and air (n = 12) were collected in seeder pigs (inoculated with M. hyopneumoniae) and contact pigs (113 days post exposure to seeder pigs). After DNA extraction, the V4 region from 16S rRNA gene was sequenced and reads were processed using Divisive Amplicon Denoising Algorithm (DADA2). Clostridium and Streptococcus were among the top five genera identified in all sample types. Mycoplasma hyopneumoniae in tracheal fluids was associated with a reduction of diversity and increment of M. hyorhinis, Glaesserella parasuis, and Pasteurella multocida in tracheal fluids, as well as a reduction of Ruminiclostridium, Barnesiella, and Lactobacillus in feces. Air contributed in a greater proportion to bacteria in the trachea compared with feces and oral fluids. In conclusion, evidence suggests the existence of complex interactions between bacterial communities from distant and distinct niches.

A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm

The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods.