Various Types Of Noise Research Articles

Fusion Based MR Images Denoising Technique Using Frequency Domain and Non-Local Means Filters

The non-invasive and non-ionizing properties of Magnetic Resonance Imaging (MRI) in addition to the associated good image quality as well as high resolution make MRI more attractive than many other medical imaging techniques. However, during the acquisition, transmission, compression and storage processes, the Magnetic Resonance (MR) images are corrupted by various types of noise and artifacts that degrade their visual quality. Most of the existing MR images denoising techniques give good quality images only when the noise density is low with their performances deteriorating as the noise power increases. The few methods that yield high quality images for all noise densities involve multiple complex and time-consuming processes. This paper proposes a computationally simple MR images denoising technique that consistently gives good denoising results for low as well as high noise densities. The proposed procedure fuses an MR image that is denoised by a Modified Discrete Fast Fourier Transform (MDFFT) filter with one that is denoised using a non-local means filter in frequency domain to yield a high quality output image. The main contribution of this proposed method is the employment of a novel image fusion approach that greatly improves the quality of the denoised image. The performance of the proposed technique is compared with those of the Wiener, median, adaptive median and the MDFFT filters. Objective metrics such as the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity (SSIM) index were used in the performance assessments. The outcomes of these assessments showed that the proposed algorithm yielded images of higher quality in terms of the PSNR measure than the existing denoising techniques by at least 7.11 dB for a noise density of up to 0.5.

Cross-Sensor Fingerprint Enhancement Using Adversarial Learning and Edge Loss.

A fingerprint sensor interoperability problem, or a cross-sensor matching problem, occurs when one type of sensor is used for enrolment and a different type for matching. Fingerprints captured for the same person using various sensor technologies have various types of noises and artifacts. This problem motivated us to develop an algorithm that can enhance fingerprints captured using different types of sensors and touch technologies. Inspired by the success of deep learning in various computer vision tasks, we formulate this problem as an image-to-image transformation designed using a deep encoder–decoder model. It is trained using two learning frameworks, i.e., conventional learning and adversarial learning based on a conditional Generative Adversarial Network (cGAN) framework. Since different types of edges form the ridge patterns in fingerprints, we employed edge loss to train the model for effective fingerprint enhancement. The designed method was evaluated on fingerprints from two benchmark cross-sensor fingerprint datasets, i.e., MOLF and FingerPass. To assess the quality of enhanced fingerprints, we employed two standard metrics commonly used: NBIS Fingerprint Image Quality (NFIQ) and Structural Similarity Index Metric (SSIM). In addition, we proposed a metric named Fingerprint Quality Enhancement Index (FQEI) for comprehensive evaluation of fingerprint enhancement algorithms. Effective fingerprint quality enhancement results were achieved regardless of the sensor type used, where this issue was not investigated in the related literature before. The results indicate that the proposed method outperforms the state-of-the-art methods.

Weighted Error Entropy-Based Information Theoretic Learning for Robust Subspace Representation.

In most of the existing representation learning frameworks, the noise contaminating the data points is often assumed to be independent and identically distributed (i.i.d.), where the Gaussian distribution is often imposed. This assumption, though greatly simplifies the resulting representation problems, may not hold in many practical scenarios. For example, the noise in face representation is usually attributable to local variation, random occlusion, and unconstrained illumination, which is essentially structural, and hence, does not satisfy the i.i.d. property or the Gaussianity. In this article, we devise a generic noise model, referred to as independent and piecewise identically distributed (i.p.i.d.) model for robust presentation learning, where the statistical behavior of the underlying noise is characterized using a union of distributions. We demonstrate that our proposed i.p.i.d. model can better describe the complex noise encountered in practical scenarios and accommodate the traditional i.i.d. one as a special case. Assisted by the proposed noise model, we then develop a new information-theoretic learning framework for robust subspace representation through a novel minimum weighted error entropy criterion. Thanks to the superior modeling capability of the i.p.i.d. model, our proposed learning method achieves superior robustness against various types of noise. When applying our scheme to the subspace clustering and image recognition problems, we observe significant performance gains over the existing approaches.

Anisotropic SpiralNet for 3D Shape Completion and Denoising.

Three-dimensional mesh post-processing is an important task because low-precision hardware and a poor capture environment will inevitably lead to unordered point clouds with unwanted noise and holes that should be suitably corrected while preserving the original shapes and details. Although many 3D mesh data-processing approaches have been proposed over several decades, the resulting 3D mesh often has artifacts that must be removed and loses important original details that should otherwise be maintained. To address these issues, we propose a novel 3D mesh completion and denoising system with a deep learning framework that reconstructs a high-quality mesh structure from input mesh data with several holes and various types of noise. We build upon SpiralNet by using a variational deep autoencoder with anisotropic filters that apply different convolutional filters to each vertex of the 3D mesh. Experimental results show that the proposed method enhances the reconstruction quality and achieves better accuracy compared to previous neural network systems.

PTrustE: A high-accuracy knowledge graph noise detection method based on path trustworthiness and triple embedding

Many knowledge graphs (KGs) constructed automatically or manually contain noises inevitably when adding heterogeneous data nowadays. The existing methods either have difficulty in detecting noisy triples with conflicted relations or ignore to represent triples after noise elimination, which creates obstacles for the downstream tasks to manipulate KGs. To detect various types of noises in KGs and constitute noise-free triples representation, this paper proposes a high-accuracy KG noise detection method based on path trustworthiness and triple embedding (PTrustE). First, PTrustE constructs a correlation-based path trustworthiness network to learn the global and local features in the path from the head entity to the tail entity of the triple. Next, PTrustE integrates all features of the path into the Bi-directional Gated Recurrent Unit to learn the path score matrix and path trustworthiness, to keep the sequential nature of paths. Finally, PTrustE uses the path score matrix for triple representation learning and the path trustworthiness for judging whether the triple is correct or not. Extensive experiments validate the superior performance of PTrustE. Compared with the second-placed INDIGO, PTrustE offers a 7.1% increase in fMRR on FB15K.

Security Protection Mechanism in Cloud Computing Authorization Model Using Machine Learning Techniques

Because of the vast number of applications and the ambiguity in application methods, handwritten character recognition has garnered widespread recognition and increased prominence in the community of pattern recognition researchers ever since it was first developed. This is due to the fact that application methods can be quite ambiguous. Computer in the cloud, on the other hand, allows for suitable network access on demand to a shared pool of customizable computing resources and digital devices. According to those knowledgeable in the subject, the standard filtering techniques are not enough when it comes to the process of denoising images. In many different approaches to machine learning, information is lost not just during the filtering process itself but also at other points during the process. When a convolutional neural network is going through its pooling operation, the internal data representation either becomes misaligned or entirely vanishes (CNN). The reconstruction of low-intensity digital photographs, which takes place during repetitive filtering, breaks away the artefacts that remain after each filtering function, which results in an image that is more uniform. The multilayer wavelet transform, or MLWT, is a method for processing features that comprises of many filter bands and is used in cloud computing authorization that is protected securely. In this scenario, a significant quantity of information gets obliterated from digital photographs during the process of feature extraction and processing. These issues are investigated by the deep learning algorithms that make use of autoencoder, and the methods also handle the novel windowing blocks that are being introduced to the layers. In this section, the magnitude and phase information is considered in order to construct a deep learning framework that will provide good denoising of digital images. The proposed architecture is equipped with the capability of accurately identifying, in real time, the noise level and type that was employed in the training of the network. The method that we have proposed, which is centred on the distribution of noise, may be used to determine the kind of noise. In order to categorise the various types of noise, we investigated nine distinct noise distributions. Dilated convolutional filtering will be used as the method of choice in order to ascertain the specific nature of the noise that can be found in the digital images. An autoencoder-based deep learning algorithm is able to accomplish numerous experimental results in digital image denoising operations that are superior to those achieved by a typical deep learning algorithm even when the intensity of the scenario is low. The performance of the method that is produced by combining the autoencoder and the dilated convolutional filtering techniques is enhanced over the performance of the technology that is currently in use. Using the method that we have outlined in this study, it is possible to recreate the low-intensity images in their entirety. We were able to show that our proposed method beat other existing algorithms for low-density digital photos by comparing several metrics, such as the peak signal-to-noise ratio (PSNR), the structural similarity index, and others (SSIM).

Neural-network quantum state tomography

We revisit the application of neural networks to quantum state tomography. We confirm that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard neural-network architecture can be adapted with our method. Our results open possibilities to use state-of-the-art deep-learning methods for quantum state reconstruction under various types of noise.

Self-Supervised Denoising for Real Satellite Hyperspectral Imagery

Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in improving hyperspectral image denoising algorithms based on supervised learning; however, these methods usually require a large number of clean/noisy training pairs, a target that is difficult to meet for real satellite hyperspectral imagery. In this paper, we propose a self-supervised learning-based algorithm, 3S-HSID, for denoising real satellite hyperspectral images without requiring external data support. The 3S-HSID framework can perform robust denoising of a single satellite hyperspectral image in all bands simultaneously. It first conducts a Bernoulli sampling of the input data, then uses the Bernoulli sampling results to construct the training pairs. Furthermore, the global spectral consistency and minimum local variance are used in the loss function to train the network. We use the training model to predict different Bernoulli sampling results, and the average of multiple predicted values is used as the denoising result. To prevent overfitting, we adopt a dropout strategy during training and testing. The results of denoising experiments on the simulated hyperspectral data show that the denoising performance of 3S-HSID is better than most state-of-the-art algorithms, especially in terms of maintaining the spectral characteristics of hyperspectral images. The denoising results for different types of real satellite hyperspectral data also demonstrate the reliability of the proposed method. The 3S-HSID framework provides a new technical means for real satellite hyperspectral image preprocessing.

Generalized gamma distribution for biomedical signals denoising

A wide range of signs are acquired from the human body called biomedical signs or biosignals, and they can be at the cell level, organ level, or sub-atomic level. Electroencephalogram is the electrical activity from the cerebrum, the electrocardiogram is the electrical activity from the heart, electrical action from the muscle sound signals referred to as electromyogram, the electroretinogram from the eye, and so on. Studying these signals can be so helpful for doctors, and it can help them examine and predict and cure many diseases.However, these signals are often affected by various types of noise. It is important to denoise the signals to get accurate information from them. The denoising process is solved by proposing an entirely novel family of flexible score functions for blind source separation, based on a family of generalized Gamma densities. To blindly extract the independent source signals, we resort to the popular fast independent component analysis (FastICA) approach; to adaptively estimate the parameters of such score functions, we use an efficient method based on maximum likelihood. The results obtained using generalized Gamma densities in our technique are better than those obtained by other distribution functions.

Hybrid neural networks for noise reductions of integrated navigation complexes

The necessity of integrated navigation complexes (INC) construction is substantiated. It is proposed to include in the complex the following inertial systems: inertial, satellite and visual. It helps to increase the accuracy of determining the coordinates of unmanned aerial vehicles. It is shown that in unfavorable cases, namely the suppression of external noise of the satellite navigation system, an increase in the errors of the inertial navigation system (INS), including through the use of accelerometers and gyroscopes manufactured using MEMS technology, the presence of bad weather conditions, which complicates the work of the visual navigation system. In order to ensure the operation of the navigation complex, it is necessary to ensure the suppression of interference (noise). To improve the accuracy of the INS, which is part of the INC, it is proposed to use the procedure for extracting noise from the raw signal of the INS, its prediction using neural networks and its suppression. To solve this problem, two approaches are proposed, the first of which is based on the use of a multi-row GMDH algorithm and single-layer networks with sigm_piecewise neurons, and the second is on the use of hybrid recurrent neural networks, when neural networks were used, which included long-term and short-term memory (LSTM) and Gated Recurrent Units (GRU). Various types of noise, that are inherent in video images in visual navigation systems are considered: Gaussian noise, salt and pepper noise, Poisson noise, fractional noise, blind noise. Particular attention is paid to blind noise. To improve the accuracy of the visual navigation system, it is proposed to use hybrid convolutional neural networks.

Image Restoration for Remote Sensing: Overview and toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.

Poly_NumtS_430 or HSA_NumtS_587 observed in massively parallel sequencing of the mitochondrial HV1 and HV2 regions

An increasing number of studies on massively parallel sequencing of mitochondrial DNA (mtDNA) have been reporting identification of various types of noise or off-target sequences. Herein, we report that an off-target haplotype (sequence length 192 bp) observed in MiSeq data of mtDNA at nucleotide position 16,209–16,400 was likely caused by polymorphic nuclear mitochondrial DNA sequences (NumtS). Buccal DNA samples from Volunteers #001–004 and Control DNA 007 were amplified with our multiplex system of the B (15,998−16,172), C (16,209−16,400), and E (30−289) regions using 2000 copies of mtDNA. A sample index was added using a Nextera XT index kit, and MiSeq Reagent Nano Kit v2 was used in 2 × 251 cycles on a MiSeq FGx. FASTQ files were analyzed by CLC Genomics Workbench 21.0.3. The extracted SAM files were analyzed using our original Excel macro to sum up the read counts as the phased variant calls for each region. An off-target haplotype differing at 19 sites against the revised Cambridge reference sequence was observed in Volunteer #001 (4 in 10 MiSeq data), Volunteer #002 (2 in 9), and Control DNA 007 (6 in 9). In a BLAST search, the sequence of the off-target haplotype matched perfectly to three polymorphic NumtS (Poly_NumtS_430 [KM281528.1], HSA_NumtS_587 [HE613849.1], and nuclear fossil [S80333.1]) and BAC clone of chromosome 11 (AC107937.2). The sequence also matched perfectly to a Filipino mtDNA (KC993973.1) which was inferred as a chimeric sequence of mtDNA and the HSA_NumtS_587. The sequence of the off-target haplotype was not contained in the latest human reference genome sequence (GRCh38.p13). In a phylogenetic tree, the off-target haplotype was genetically distant from modern human mtDNA and not directly connected to them. In conclusion, observed off-target haplotype amplified by our multiplex system was likely caused by Poly_NumtS_430 or HSA_NumtS_587.

Noise exposure and risk of myocardial infarction incidence and mortality: a dose-response meta-analysis.

The strength and shape of the dose-response relationship between different types of noise and myocardial infarction (MI) remain unclear. Therefore, we aimed to summarize the evidence of the association between various types of noise and MI incidence and mortality through a dose-response meta-analysis. We performed a systematic search of the PubMed, Embase, and Web of Science databases up to December 19, 2021. The generalized least-squares method and restricted cubic splines were used to assess the potential linear and nonlinear dose-response relationships between noise exposure and the risk of MI events. Twenty observational studies with 34 reports met the eligibility criteria. In the linear models, the pooled relative risk and corresponding 95% confidence interval (CI) for MI incidence was 1.04 (95% CI: 1.02 - 1.05), and the MI mortality was 1.02 (95% CI: 1.02 - 1.03) for each 10dB(A) increase in noise exposure. In addition, we observed an approximately J-shaped dose-response relationship between noise and MI mortality (Pnonlinearity = 0.0037), whereas the threshold for the statistical impact of noise on MI mortality may be 42dB(A). Our findings support the notion that various types of noise exposure have a positive dose-response relationship with the risk of MI incidence and mortality.

Two-point Landweber-type method with convex penalty terms for nonsmooth nonlinear inverse problems

Abstract In this work, we propose a two-point Landweber-type method with general convex penalty terms for solving nonsmooth nonlinear inverse problems. The design of our method can cope with nonsmooth nonlinear inverse problems or the nonlinear inverse problems whose data are contaminated by various types of noise. The method consists of the two-point acceleration strategy and inner solvers. Inner solvers are used to solve the minimization problems with respect to the penalty terms in each steps. If the minimization problems can be solved explicitly, the inner solvers will be chosen to be the exact solvers. Otherwise, we will use inexact solvers as inner solvers. Convergence results are given without utilizing the Gâteaux differentiability of the forward operator or the reflexivity of the image space. Numerical simulations are given to test the performance of the proposed method.

Urbanization effect on Hyderabad seismic station

Seismographs record earthquakes and also record various types of noise, including anthropogenic noise. In the present study, we analyse the influence of the lockdown due to COVID-19 on the ground motion at CSIR-NGRI HYB Seismological Observatory, Hyderabad. We analyse the noise recorded a week before and after the implementation of lockdown by estimating the probability density function of seismic power spectral density and by constructing the daily spectrograms. We find that at low frequency (<1 Hz), where the noise is typically dominated by naturally occurring microseismic noise, a reduction of ~2 dB for secondary microseisms (7–3 s) and at higher frequency (1–10 Hz) a reduction of ~6 dB was observed during the lockdown period. The reduction in higher frequencies corresponding to anthropogenic noise sources led to improving the SNR (signal-to-noise ratio) by a factor of 2 which is the frequency bandwidth of the microearthquakes leading to the identification of microearthquakes with Ml around 3 from epicentral distances of 180 km.

Data-driven modeling of time-domain induced polarization

Induced polarization (IP) measurements are affected by various types of noise, which should be removed prior to data interpretation. However, existing data processing methods often rely on empirical assumptions about the standard shape of IP decay curves. Our goal is to introduce a data-driven approach for modeling and processing time-domain IP measurements. To reach this goal, we train a variational autoencoder (VAE) on 1,600,319 IP decays collected in Canada, the United States, and Kazakhstan. The proposed deep learning approach is unsupervised and avoids the pitfalls of IP parameterization with empirical Cole-Cole and Debye decomposition models, simple power-law models, or mechanistic models. Four applications of VAEs are key to modeling and processing IP data: (1) synthetic data generation, (2) Bayesian denoising, (3) evaluation of signal-to-noise ratio, and (4) outlier detection. Furthermore, we interpret the IP data compilation’s latent representation and reveal a correlation between its first dimension and the average chargeability. Finally, we determine that a single real-valued scalar parameter contains sufficient information to encode IP data. This new finding suggests that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified. A pretrained implementation of the VAE model is available as open-source Python code.

Two Ways to Improve Average and Median Filters

Colored digital images are exposed to various types of noise, the most important of which is salt and pepper noise, which negatively affects the clarity of the image in addition to the negative effects on its characteristics, which calls for providing an effective method that mitigates the negative effects of noise as much as possible. The two filters average and median are considered two of the best filters used to remove salt and pepper noise or mitigate the negative effects of this noise, especially if the noise density is low. However, when this density rises, these filters become ineffective in dealing with the noise. In this paper research we will introduce two ways of improving the operations of average and median filters, these improved filters will be tested and compared with the standard average and median filters, it will be shown how the improvements will enhance the value of the quality parameters MSE and PSNR, some recommendation will be raised to show when and how to use each of the improvement ways.

Wind Turbine Noise Behaviorally and Physiologically Changes Male Frogs

Simple SummaryWe analyzed the behavioral–physiological–immunological interconnected process of Japanese tree frogs (Dryophytes japonicus) during their breeding season when exposed to wind turbine noise. Frogs collected from paddy fields with wind power generators exhibited a faster call rate, higher salivary concentrations of corticosterone, and lower innate immunity. However, frogs exposed to the turbine noise for a short period of time only showed faster call rates. An increase in corticosterone was correlated with an increase in call rate and a decrease in immunity. It seems that the need for energy mobilization due to an increase in the call rate leads to a decrease in innate immunity through an increase in corticosterone. The decrease in immunity due to energy tradeoff or physiological response can change disease epidemiology in the population and create new adaptive patterns within these habitats.As the advantages of wind energy as an eco-friendly strategy for power generation continue to be revealed, the number of offshore wind farms also increases worldwide. However, wind turbines can induce behavioral and physiological responses in animals by emitting various types of noises. In this study, we investigated the behavioral, physiological, and immunological responses of male Japanese tree frogs (Dryophytes japonicus) when exposed to wind turbine noise. To determine the effects during the breeding season, frogs were collected from areas with and without wind turbines. Additionally, we exposed the frogs to recorded wind turbine noise at a site without a wind generator for 1 h to 24 h to analyze the short-term effects. Three types of calling patterns (dominant frequency, note duration, and call rate) were analyzed to investigate behavioral responses. Physiological responses were assessed using two steroid hormones assays, namely testosterone and corticosterone detection in the saliva. The immunity of each individual was assessed using a bacterial killing assay. The wind turbine group in the field had a higher call rate and corticosterone levels and lower immunity than the group in the field without turbines present, and all three of these variables were correlated with each other. Conversely, in the noise exposure experiment, a higher call rate was only observed post-exposure compared to pre-exposure. Thus, turbine noise seems to induce decreased immunity in Japanese tree frogs as an increase in energy investment that triggers a behavioral response rather than acting as a sole physiological response that leads to a direct increase in corticosterone. This decreased immunity due to energy tradeoff or physiological response can change the disease epidemiology of the population and create new adaptive patterns in these habitats.

Denoising of Fourier domain quantum optical coherence tomography spectrums based on deep-learning methods

One of the promising optical coherence tomography schemes using quantum light is the Fourier domain quantum optical coherence tomography. It combines the advantage of quantum metrology and the optical coherence tomography, providing a great detection with a high axial resolution. However, the application of the Fourier domain quantum optical coherence tomography is hard to implement since various types of noise would affect the quality and finally eliminate the advantages. In this paper, we quantitively analyze the affection of the noise induced by quantum fluctuation on Fourier domain quantum optical coherence tomography and propose to suppress the effect by deep-learning method. Our simulation shows that it could severely lower the accuracy of the detection, and can be removed by our deep-learning model. We believe that our results will promote the application of similar quantum optical coherence tomography strategies to real scenarios.

IEHC: An efficient image encryption technique using hybrid chaotic map

Image encryption is essential for the transmission of the sensitive images through insecure channels used in wired or wireless communication nowadays. With the advent of cloud enabled surveillance applications, Internet of Things applications, social media communications, the requirement of image encryption is also increasing day by day. The conventional encryption schemes demand high resources in terms of processing power, memory and bandwidth resulting degradation in overall system response time. In this paper, first a novel shuffling technique has been introduced to shuffle the pixels of an input color image. Subsequently, this paper proposes an image cipher that uses Logistic map and Arnold's cat map for encryption of color images. The experimental results and various security analyses exhibit good security characteristics and also prove its robustness against various types of noises, data loss and cryptanalysis attacks, making it appropriate for implementing in real-time applications.