Showing papers on "Noise measurement published in 2014"

Classification in the Presence of Label Noise: A Survey

Abstract: Label noise is an important issue in classification, with many potential negative consequences. For example, the accuracy of predictions may decrease, whereas the complexity of inferred models and the number of necessary training samples may increase. Many works in the literature have been devoted to the study of label noise and the development of techniques to deal with label noise. However, the field lacks a comprehensive survey on the different types of label noise, their consequences and the algorithms that consider label noise. This paper proposes to fill this gap. First, the definitions and sources of label noise are considered and a taxonomy of the types of label noise is proposed. Second, the potential consequences of label noise are discussed. Third, label noise-robust, label noise cleansing, and label noise-tolerant algorithms are reviewed. For each category of approaches, a short discussion is proposed to help the practitioner to choose the most suitable technique in its own particular field of application. Eventually, the design of experiments is also discussed, what may interest the researchers who would like to test their own algorithms. In this paper, label noise consists of mislabeled instances: no additional information is assumed to be available like e.g., confidences on labels.

Exposing Region Splicing Forgeries with Blind Local Noise Estimation

Abstract: Region splicing is a simple and common digital image tampering operation, where a chosen region from one image is composited into another image with the aim to modify the original image's content. In this paper, we describe an effective method to expose region splicing by revealing inconsistencies in local noise levels, based on the fact that images of different origins may have different noise characteristics introduced by the sensors or post-processing steps. The basis of our region splicing detection method is a new blind noise estimation algorithm, which exploits a particular regular property of the kurtosis of nature images in band-pass domains and the relationship between noise characteristics and kurtosis. The estimation of noise statistics is formulated as an optimization problem with closed-form solution, and is further extended to an efficient estimation method of local noise statistics. We demonstrate the efficacy of our blind global and local noise estimation methods on natural images, and evaluate the performances and robustness of the region splicing detection method on forged images.

Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization

Abstract: Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.

Low-rank approximation based multichannel wiener filter algorithms for noise reduction with application in cochlear implants

Abstract: This paper presents low-rank approximation based multichannel Wiener filter algorithms for noise reduction in speech plus noise scenarios, with application in cochlear implants. In a single speech source scenario, the frequency-domain autocorrelation matrix of the speech signal is often assumed to be a rank-1 matrix, which then allows to derive different rank-1 approximation based noise reduction filters. In practice, however, the rank of the autocorrelation matrix of the speech signal is usually greater than one. Firstly, the link between the different rank-1 approximation based noise reduction filters and the original speech distortion weighted multichannel Wiener filter is investigated when the rank of the autocorrelation matrix of the speech signal is indeed greater than one. Secondly, in low input signal-to-noise-ratio scenarios, due to noise non-stationarity, the estimation of the auto-correlation matrix of the speech signal can be problematic and the noise reduction filters can deliver unpredictable noise reduction performance. An eigenvalue decomposition based filter and a generalized eigenvalue decomposition based filter are introduced that include a more robust rank-1, or more generally rank-R, approximation of the autocorrelation matrix of the speech signal. These noise reduction filters are demonstrated to deliver a better noise reduction performance especially in low input signal-to-noise-ratio scenarios. The filters are especially useful in cochlear implants, where more speech distortion and hence a more aggressive noise reduction can be tolerated.

Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image

Abstract: The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.

Dynamic noise aware training for speech enhancement based on deep neural networks.

Abstract: We propose three algorithms to address the mismatch problem in deep neural network (DNN) based speech enhancement. First, we investigate noise aware training by incorporating noise informationin the testutterance with anideal binary maskbased dynamic noise estimation approach to improve DNN’s speech separation ability from the noisy signal. Next, a set of more than 100 noise types is adopted to enrich the generalization capabilities of the DNN to unseen and non-stationary noise conditions. Finally, the quality of the enhanced speech can further be improved by global variance equalization. Empirical results show that each of the three proposed techniques contributes to the performance improvement. Compared to the conventional logarithmic minimum mean squared error speech enhancement method, our DNN system achieves 0.32 PESQ (perceptual evaluation of speech quality) improvement across six signal-tonoise ratio levels ranging from -5dB to 20dB on a test set with unknown noise types. We also observe that the combined strategies can well suppress highly non-stationary noise better than all the competing state-of-the-art techniques we have evaluated. Index Terms: Speech enhancement, deep neural networks, noise aware training, ideal binary mask, non-stationary noise

Noise cancelling of MRS signals combining model-based removal of powerline harmonics and multichannel Wiener filtering

Abstract: SUMMARY The fidelity of magnetic resonance sounding signals is often severely degraded by noise, primarily electrical interference from powerline harmonics and short electromagnetic discharges. In many circumstances, the noise originates from multiple sources. We show that noise cancelling can be improved if the multiple origins of noise are taken into account. In particular, a method is developed where powerline harmonics are efficiently removed through a modelbased approach. Subsequently, standard multichannel Wiener filtering can be used to provide a further noise reduction. The performance of the method depends on the distribution of noise on the particular site of measurement. Simulations on synthetic signals embedded in real noise recordings show that the combined approach can improve the signal-to-noise ratio with an accompanying improvement in retrieval of model parameters.

Experimental Study of Slat Noise from 30P30N Three-Element High-Lift Airfoil in JAXA Hard-Wall Low-Speed Wind Tunnel

Abstract: Aeroacoustic measurements associated with noise radiation from the leading edge slat of the canonical, unswept 30P30N three-element high-lift airfoil configuration have been obtained in a 2 m x 2 m hard-wall wind tunnel at the Japan Aerospace Exploration Agency (JAXA). Performed as part of a collaborative effort on airframe noise between JAXA and the National Aeronautics and Space Administration (NASA), the model geometry and majority of instrumentation details are identical to a NASA model with the exception of a larger span. For an angle of attack up to 10 degrees, the mean surface Cp distributions agree well with free-air computational fluid dynamics predictions corresponding to a corrected angle of attack. After employing suitable acoustic treatment for the brackets and end-wall effects, an approximately 2D noise source map is obtained from microphone array measurements, thus supporting the feasibility of generating a measurement database that can be used for comparison with free-air numerical simulations. Both surface pressure spectra obtained via KuliteTM transducers and the acoustic spectra derived from microphone array measurements display a mixture of a broad band component and narrow-band peaks (NBPs), both of which are most intense at the lower angles of attack and become progressively weaker as the angle of attack is increased. The NBPs exhibit a substantially higher spanwise coherence in comparison to the broadband portion of the spectrum and, hence, confirm the trends observed in previous numerical simulations. Somewhat surprisingly, measurements show that the presence of trip dots between the stagnation point and slat cusp enhances the NBP levels rather than mitigating them as found in a previous experiment.

Capacity of the Memoryless Additive Inverse Gaussian Noise Channel

Abstract: The memoryless additive inverse Gaussian noise channel model describing communication based on the exchange of chemical molecules in a drifting liquid medium is investigated for the situation of simultaneously an average-delay and a peak-delay constraint. Analytical upper and lower bounds on its capacity in bits per molecule use are presented. These bounds are shown to be asymptotically tight, i.e., for the delay constraints tending to infinity with their ratio held constant (or for the drift velocity of the fluid tending to infinity), the asymptotic capacity is derived precisely. Moreover, characteristics of the capacity-achieving input distribution are derived that allow accurate numerical computation of capacity. The optimal input appears to be a mixed continuous and discrete distribution.

On impulse noise and its models

Abstract: This article gives a discussion on impulse noise, its models and how it affects communications systems. We discuss the different impulse noise models in the literature, looking at their similarities and differences in communications systems. The impulse noise models discussed are memoryless (Middleton Class A and Bernoulli-Gaussian), and with memory (Markov- Middleton and Markov-Gaussian). We then go further to give performance comparisons in terms of bit error rates for some of the variants of impulse noise models. We also compare the bit error rate performance of single-carrier (SC) and multi-carrier (MC) communications systems operating under impulse noise. It can be seen that MC is not always better than SC under impulse noise. Lastly, the known impulse noise mitigation schemes (clipping/nulling using thresholds, iterative based and error control coding methods) are discussed.

A Unifying Model for External Noise Sources and ISI in Diffusive Molecular Communication

Abstract: This paper considers the impact of external noise sources, including interfering transmitters, on a diffusive molecular communication system, where the impact is measured as the number of noise molecules expected to be observed at a passive receiver. A unifying model for noise, multiuser interference, and intersymbol interference is presented, where, under certain circumstances, interference can be approximated as a noise source that is emitting continuously. The model includes the presence of advection and molecule degradation. The time-varying and asymptotic impact is derived for a series of special cases, some of which facilitate closed-form solutions. Simulation results show the accuracy of the expressions derived for the impact of a continuously-emitting noise source, and show how approximating old intersymbol interference as a noise source can simplify the calculation of the expected bit error probability of a weighted sum detector.

Uncertainty Management in Differential Evolution Induced Multiobjective Optimization in Presence of Measurement Noise

Abstract: This paper aims to design new strategies to extend traditional multiobjective optimization algorithms to efficiently obtain Pareto-optimal solutions in presence of noise on the objective surfaces. The first strategy, referred to as adaptive selection of sample size, is employed to balance the tradeoff between quality measure of fitness and run-time complexity. The second strategy is concerned with determining statistical expectation, instead of conventional averaging, of fitness samples as the measure of fitness of the trial solutions. The third strategy attempts to extend Goldberg's method to compare slightly worse trial solutions with its competitor by a more statistically viable comparator to examine possible placement of the former solution in the Pareto optimal front. The traditional differential evolution for multiobjective optimization algorithm has been modified by extending its selection step with the proposed strategies. Experiments undertaken to study the performance of the extended algorithm reveal that the extended algorithm outperforms its competitors with respect to three performance metrics, when examined on a test suite of 23 standard benchmarks with additive noise of three statistical distributions. The extended algorithm has been applied on the well known box-pushing problem, where the forces and torques required to shift the box by two robots are evaluated to jointly satisfy the conflicting objectives on task-execution time and energy consumption in presence of noise on range estimates from the sidewalls of the workspace. The application justifies the importance of the proposed noise-handling strategies in practical systems.

Frequency Estimation of Distorted and Noisy Signals in Power Systems by FFT-Based Approach

Abstract: This paper focuses on the accurate frequency estimation of power signals corrupted by a stationary white noise. The noneven item interpolation FFT based on the triangular self-convolution window is described. A simple analytical expression for the variance of noise contribution on the frequency estimation is derived, which shows the variances of frequency estimation are proportional to the energy of the adopted window. Based on the proposed method, the noise level of the measurement channel can be estimated, and optimal parameters (e.g., sampling frequency and window length) of the interpolation FFT algorithm that minimize the variances of frequency estimation can thus be determined. The application in a power quality analyzer verified the usefulness of the proposed method.

Noise-Robust Texture Description Using Local Contrast Patterns via Global Measures

Abstract: This letter presents a noise-robust descriptor by exploring a set of local contrast patterns (LCPs) via global measures for texture classification. To handle image noise, the directed and undirected difference masks are designed to calculate three types of local intensity contrasts: directed, undirected, and maximum difference responses. To describe pixel-wise features, these responses are separately quantized and encoded into specific patterns based on different global measures. These resulting patterns (i.e., LCPs) are jointly encoded to form our final texture representation. Experiments are conducted on the well-known Outex and CUReT databases in the presence of high levels of noise. Compared to many state-of-the-art methods, the proposed descriptor achieves superior texture classification performance while enjoying a compact feature representation.

Ensemble-based noise detection: noise ranking and visual performance evaluation

Abstract: Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of noise detection algorithms on data with randomly injected noise. A methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new noise detection algorithms into the proposed noise detection and performance evaluation workflows.

Noise parameter mismatch in variance stabilization, with an application to Poisson-Gaussian noise estimation.

Abstract: In digital imaging, there is often a need to produce estimates of the parameters that define the chosen noise model. We investigate how the mismatch between the estimated and true parameter values affects the stabilization of variance of signal-dependent noise. As a practical application of the general theoretical considerations, we devise a novel approach for estimating Poisson–Gaussian noise parameters from a single image, combining variance-stabilization and noise estimation for additive Gaussian noise. Furthermore, we construct a simple algorithm implementing the devised approach. We observe that when combined with optimized rational variance-stabilizing transformations, the algorithm produces results that are competitive with those of a state-of-the-art Poisson–Gaussian estimator.

Voltage Noise in Multi-Core Processors: Empirical Characterization and Optimization Opportunities

Abstract: Voltage noise characterization is an essential aspect of optimizing the shipped voltage of high-end processor based systems. Voltage noise, i.e. Variations in the supply voltage due to transient fluctuations on current, can negatively affect the robustness of the design if it is not properly characterized. Modeling and estimation of voltage noise in a pre-silicon setting is typically inadequate because it is difficult to model the chip/system packaging and power distribution network (PDN) parameters very precisely. Therefore, a systematic, direct measurement-based characterization of voltage noise in a post-silicon setting is mandatory in validating the robustness of the design. In this paper, we present a direct measurement-based voltage noise characterization of a state-of-the-art mainframe class multicoreprocessor. We develop a systematic methodology to generate noise stress marks. We study the sensitivity of noise in relation to the different parameters involved in noise generation: (a) stimulus sequence frequency, (b) supply current delta, (c) number of noise events and, (d) degree of alignment or synchronization of events in a multi-core context. By sensing per-core noise in amulti-core chip, we characterize the noise propagation across the cores. This insight opens up new opportunities for noise mitigation via workload mappings and dynamic voltage guard banding.

Performance Study of the MVDR Beamformer as a Function of the Source Incidence Angle

Abstract: Linear microphone arrays combined with the minimum variance distortionless response (MVDR) beamformer have been widely studied in various applications to acquire desired signals and reduce the unwanted noise. Most of the existing array systems assume that the desired sources are in the broadside direction. In this paper, we study and analyze the performance of the MVDR beamformer as a function of the source incidence angle. Using the signal-to-noise ratio (SNR) and beampattern as the criteria, we investigate its performance in four different scenarios: spatially white noise, diffuse noise, diffuse-plus-white noise, and point-source-plus-white noise. The results demonstrate that the optimal performance of the MVDR beamformer occurs when the source is in the endfire directions for diffuse noise and point-source noise while its SNR gain does not depend on the signal incidence angle in spatially white noise. This indicates that most current systems may not fully exploit the potential of the MVDR beamformer. This analysis does not only help us better understand this algorithm, but also helps us design better array systems for practical applications.

Comparison and optimal parameter settings of reference‐based harmonic noise cancellation in time and frequency domains for surface‐NMR

Abstract: The technique of surface nuclear magnetic resonance (surface-NMR) provides information on porosity and hydraulic conductivity that is highly valuable in a hydrogeological context. However, the applicability of surface-NMR is often limited due to a bad signal-to-noise ratio. In this paper we provide a detailed insight into the technique of harmonic noise cancellation based on remote references to improve the signal-to-noise ratio. We give numerous synthetic examples to study the influence of various parameters such as optimal filter length for time- domain approaches or the necessary record length for frequency-domain approaches, all of which evaluated for different types of noise conditions. We show that the frequency-domain approach is superior to time-domain approaches. We demonstrate that the parameter settings in the frequency domain and the decision whether or not to use separated noise measurement depend on the actual noise properties, i.e., frequency content or stability with time. We underline our results using two field examples.

High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial.

Abstract: In many applications, such as development and testing of image processing algorithms, it is often necessary to simulate images containing realistic noise from solid-state photosensors. A high-level model of CCD and CMOS photosensors based on a literature review is formulated in this paper. The model includes photo-response non-uniformity, photon shot noise, dark current Fixed Pattern Noise, dark current shot noise, offset Fixed Pattern Noise, source follower noise, sense node reset noise, and quantisation noise. The model also includes voltage-to-voltage, voltage-to-electrons, and analogue-to-digital converter non-linearities. The formulated model can be used to create synthetic images for testing and validation of image processing algorithms in the presence of realistic images noise. An example of the simulated CMOS photosensor and a comparison with a custom-made CMOS hardware sensor is presented. Procedures for characterisation from both light and dark noises are described. Experimental results that confirm the validity of the numerical model are provided. The paper addresses the issue of the lack of comprehensive high-level photosensor models that enable engineers to simulate realistic effects of noise on the images obtained from solid-state photosensors.

Random-valued impulse noise removal using fuzzy weighted non-local means

Abstract: In this paper, we propose a fuzzy weighted non-local means filter for the removal of random-valued impulse noise. We introduce a new fuzzy weighting function, which can shut off the impulsive weight effectively, to the non-local means. According to the new weighting function, the more a pixel is corrupted, the less it is exploited to reconstruct image information. Experiments show that the performances of the new filter are surprisingly satisfactory in terms of both visual quality and quantitative measurement. Moreover, our filter also can be used to remove mixed Gaussian and random-valued impulse noise.

An Amplitude-Preserved Time–Frequency Peak Filtering Based on Empirical Mode Decomposition for Seismic Random Noise Reduction

Abstract: Time-frequency peak filtering (TFPF) is a classical filtering method in time-frequency domain. It applies Wigner-Ville distribution to estimate the instantaneous frequency of an analytical signal. There is a pair of contradiction in this method, i.e., selecting a short window length may lead to good preservation for signal amplitude but bad random noise reduction whereas selecting a long window length may lead to serious attenuation for signal amplitude but effective random noise reduction. In order to make a good tradeoff between valid signal amplitude preservation and random noise reduction, we adopt empirical mode decomposition (EMD) to improve the TFPF results. The new idea is to utilize the decomposition characteristic of EMD which decomposes a signal to several modes from high to low frequency and to take advantage of the time-frequency filtering characteristic of TFPF which can recognize the valid signal component in the time-frequency plane in order to achieve effective random noise reduction together with good amplitude preservation. Through some experiments on synthetic seismic models and field seismic records, we show the better performance of the new method compared with the conventional TFPF.

Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring

Abstract: This paper presents a new approach for cutting force denoising in micro-milling condition monitoring. In micro-milling, the comparatively small cutting force signal is contaminated by heavy noise, and as a result, it is necessary to denoise the force signal before further processing it. The traditional denoising methods, based on Gaussian noise assumption, are not effective in this situation because the noise is found to contain high non-Gaussian component. Based on the force and noise's sparse structures in the time–frequency domain, this approach employs a sparse decomposition approach and solves denoising as a convex optimization problem. It is shown that the proposed approach can separate the heavy non-Gaussian noise and recover useful information for condition monitoring.

Variable-Eccentricity Hyperbolic-Trace TFPF for Seismic Random Noise Attenuation

Abstract: Seismic noise attenuation to improve signal-to-noise ratio plays an important role in seismic data processing In recent years, time-frequency peak filtering (TFPF) has been introduced and applied to seismic random noise attenuation successfully However, in the conventional TFPF, the window length (WL) is fixed and used for all frequency components As a consequence, serious loss of the effective components is unavoidable due to the inappropriate WL The recently proposed radial-trace TFPF adapts radial-trace transform to reduce the dominant frequencies of the effective signals Nevertheless, the radial traces with a fixed inclination angle have some limitations for bent reflection events To resolve these shortcomings, this paper presents a novel variable-eccentricity hyperbolic-trace TFPF In this novel method, the noisy record is first resampled along a family of spatial–temporal hyperbolic filtering traces of different bending degrees In this way, the spatial correlation between the adjacent channels is taken into account, the linearity of the input signals is enhanced, and the estimation bias of the instantaneous frequency is reduced Moreover, there is little difference between the reduced dominant frequencies A fixed WL is suitable for all reduced dominant frequencies without distortion of the effective components Finally, we evaluate the performance of our method on some synthetic records and field data The experimental results illustrate that our proposed method attenuates random noise effectively and recovers the effective reflection events smoothly and more continuously compared with the other methods