Noise Estimation Method Research Articles

Comparative performance evaluation of MMSE-based speech enhancement techniques through simulation and real-time implementation

In this article, a generic model of minimum mean square error (MMSE) based speech enhancement technique has been presented and implemented on the hardware. Models for different MMSE-based methods were obtained by changing the gain function in the generic model. In all the methods, modified cascaded median-based noise estimation method has been used for noise estimation. Performances of all these MMSE-based methods were compared, among themselves and also with the spectral subtraction method for speech enhancement. The results have been evaluated using objective measures, subjective measure and composite objective measures for different noisy speech files. Results, in terms of objective evaluation parameters, indicated that the adaptive β-order MMSE method yielded better performance than the other methods. In subjective quality test (according to MOS listening test), β-order MMSE and adaptive β-order MMSE method yielded high scores. Real-time implementation has been carried out using TMS320C6416T DSP starter kit and code composer studio software. Estimation of memory consumption and execution time has been done for all the methods.

A method to extract realistic artifacts from electrocardiogram recordings for robust algorithm testing

ObjectiveRecordings of signal noise and artifacts can be added to clean electrocardiogram (ECG) records to assess the performance of ECG and arrhythmia analysis algorithms in the presence of noise. We present a method to estimate device-specific signal noise and artifacts from ECG records. This method can be applied to obtain noise estimates from healthy subjects on any ECG lead, allowing a simple device-specific recording. The proposed approach is assessed using the MIT-BIH Noise Stress Test Database recordings combined with simulated ECGs. MethodsThe proposed noise-estimation method is based on the subtraction of a time-aligned median beat from a noisy ECG recording. To test our method, electrode motion and muscle artifact noise from MIT-BIH Noise Stress Test database were added to simulated ECG signals at signal-noise ratios (SNR) from −6 to 20 dB. A comparison between noise and estimated noise signal statistical characteristics was made including root-mean squared error and assessment of the power content in three frequency bands (cardiac [0.5–5 Hz], mid [5–25 Hz], and high [25–40 Hz]). ResultsVisual assessment and frequency analysis demonstrate the good quality of noise estimation. Root-mean squared error between noise and estimated noise signals was <0.5 Normalized Units across all SNR levels. Band power error was stable across SNR levels with median percentage error between noise and estimate noise signals of <10% for cardiac and mid frequency bands. ConclusionEstimating noise from ECG records is a viable approach to generate noise and artifacts-only signals. These signals are device-specific and easy to collect from healthy subjects without requiring special electrode set-ups. Therefore, they may be suitable for use with annotated ECG databases to assess the robustness of ECG analysis algorithms in the presence of noise.

Effective and Fast Estimation for Image Sensor Noise Via Constrained Weighted Least Squares.

Noise estimation is crucial in many image processing algorithms such as image denoising. Conventionally, the noise is assumed as a signal-independent additive white Gaussian process. However, for the real raw data of image sensor, the present noise should be practically modeled as signal dependent. In this paper, we propose an effective and fast image sensor noise estimation method for a single raw image. The noise model parameters are estimated via constrained weighted least squares (WLS) fitting on a number of data samples, each of which is generated from a group of weakly textured patches. Specifically, we first design a fast scheme for selecting weakly textured patches, with the guidance of image histogram. To robustly fit the data samples, we then explicitly account for the credibility of each sample by measuring the texture strength of the grouped patches. The image sensor noise estimation is finally formulated as a constrained WLS optimization problem, which can be solved efficiently. Experimental results demonstrate that our method could run much faster than the existing schemes, while retaining the state-of-the-art estimation performance.

Research on the correlation between the frequency‐domain characteristics of corona current and A‐weighted sound level of audible noise for UHV DC transmission line

Audible noise is closely related to the frequency-domain characteristics of corona current. Based on the largest ultra-high-voltage (UHV) DC corona cage in the world and an actual UHV DC transmission line, a large amount of audible noise and corona current were measured simultaneously here. According to the statistical analysis of the experimental data, the correlation relationship between the frequency-domain characteristics of corona current and audible noise was obtained, and the causes of correlation rule were also analysed. Meanwhile, the correlation formula between the spectrum characteristics of corona current and A-weighted sound level of audible noise was discovered. The experimental results showed that the mathematical correlation formula discovered has good universality. The obtained results can provide theory and experimental foundation for deeply research on the essential connection between corona current and audible noise, which makes it possible to explore the causes of audible noise's change law from the perspective of ‘sound source’ and may provide a new method for audible noise estimation.

Study on Wind Noise Estimation Method Included in Measurement Data of Low Frequency Sound

Study on Wind Noise Estimation Method Included in Measurement Data of Low Frequency Sound

Patient‐specific estimation of spatially variant image noise for a pinhole cardiacSPECTcamera

New single photon emission computed tomography (SPECT) cameras using fixed pinhole collimation are increasingly popular. Pinhole collimators are known to have variable sensitivity with distance and angle from the pinhole aperture. It follows that pinhole SPECT systems will also have spatially variant sensitivity and hence spatially variant image noise. The objective of this study was to develop and validate a rapid method for analytically estimating a map of the noise magnitude in a reconstructed image using data from a single clinical acquisition. The projected voxel (PV) noise estimation method uses a modified forward projector with attenuation effects to estimate the number of photons detected from each voxel in the field-of-view. We approximate the noise for each voxel as the standard deviation of a Poisson distribution with a mean equal to the number of detected photons. An empirical formula is used to address scaling discrepancies caused by image reconstruction. Calibration coefficients are determined for the PV method by comparing it with noise measured from a nonparametrically bootstrapped set of images of a spherical uniformly filled Tc-99m water phantom. Validation studies compare PV noise estimates with bootstrapped measured noise for 31 patient images (5min, 340MBq, 99m Tc-tetrofosmin rest study). Bland-Altman analysis shows R2 correlations ≥70% between the PV-estimated and -measured image noise. For the 31 patient cardiac images, the PV noise estimate has an average bias of 0.1% compared to bootstrapped noise and have a coefficient of variation (CV)≤17%. The bootstrap approach to noise measurement requires 5h of computation for each image, whereas the PV noise estimate requires only 64s. In cardiac images, image noise due to attenuation and camera sensitivity varies on average from 4% at the apex to 9% in the basal posterior region of the heart. The standard deviation between 15 healthy patient study images (including physiological variability in the population) ranges from 6% to 16.5% over the length of the heart. The PV method provides a rapid estimate for spatially variant patient-specific image noise magnitude in a pinhole-collimated dedicated cardiac SPECT camera with a bias of -0.3% and better than 83% precision.

Poisson-Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain.

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.

Noise Estimation and Quality Assessment of Gaussian Noise Corrupted Images

Evaluating the exact quantity of noise present in an image and quality of an image in the absence of reference image is a challenging task. We propose a near perfect noise estimation method and a no reference image quality assessment method for images corrupted by Gaussian noise. The proposed methods obtain initial estimate of noise standard deviation present in an image using the median of wavelet transform coefficients and then obtains a near to exact estimate using curve fitting. The proposed noise estimation method provides the estimate of noise within average error of +/-4%. For quality assessment, this noise estimate is mapped to fit the Differential Mean Opinion Score (DMOS) using a nonlinear function. The proposed methods require minimum training and yields the noise estimate and image quality score. Images from Laboratory for image and Video Processing (LIVE) database and Computational Perception and Image Quality (CSIQ) database are used for validation of the proposed quality assessment method. Experimental results show that the performance of proposed quality assessment method is at par with the existing no reference image quality assessment metric for Gaussian noise corrupted images.

RENOIR – A dataset for real low-light image noise reduction

Image denoising algorithms are evaluated using images corrupted by artificial noise, which may lead to incorrect conclusions about their performances on real noise. In this paper we introduce a dataset of color images corrupted by natural noise due to low-light conditions, together with spatially and intensity-aligned low noise images of the same scenes. We also introduce a method for estimating the true noise level in our images, since even the low noise images contain small amounts of noise. We evaluate the accuracy of our noise estimation method on real and artificial noise, and investigate the Poisson-Gaussian noise model. Finally, we use our dataset to evaluate six denoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer Perceptron, and two versions of NL-means. We show that while the Multi-Layer Perceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on gray images with synthetic noise, they lag behind on our dataset.

Adaptive Subspace Method for Phase Noise Estimation in CO-OFDM Systems

In this paper, a blind phase noise estimation method for a coherent optical orthogonal frequency-division multiplexing (CO-OFDM) system is considered. Based on the subspace approach, a simple and robust method is proposed to adaptively estimate and track phase noise in CO-OFDM systems. The idea behind this novel technique is to estimate the singular vectors that correspond to the smallest singular values of the noise subspace. A weighting parameter, which is derived based on the forward–backward linear prediction technique, is subsequently constructed using the obtained singular vectors of the noise subspace to adaptively estimate the phase noise in the system. Also, a variable step size is introduced to ensure an improved performance as well as stable convergence. Simulation results are shown to demonstrate the effectiveness as well as the efficiency of the proposed algorithm, which is implemented in a polarization multiplexed CO-OFDM system stressed by polarization mode dispersion, chromatic dispersion, and other polarization-dependent losses along the fiber link.

COMPARATIVE NOISE ESTIMATION METHOD FOR GAS TURBINE POWER PLANTS OF JET PLANES

A noise estimation method for comparison of gas turbine power plants of jet planes is described. The method is based on certified acoustic tests of jet planes with a configuration of engines on pylons under a wing. This approach allows us to choose a newly developed airplane from the existing fleet of those engines which, as a part of a power plant, can produce the least environmental noise levels at take-off in terms of EPNdB. The dependence of noise level of airplane power plants on the bypass ratio and take-off thrust of engines is considered. Three groups of power plants are determined based on the "bypass ratio" parameter. Within each group, the power plants with minimum noise level are presented. The approximation dependencies of minimum noise levels on the engine thrust for a twin-engine layout are found for each group of power plants. The excess of the least noise levels of modern power plants is determined relative to the minimum levels of noise reached to the present time. The minimum levels can be an estimation of acoustic perfection degree of the power plants in the group.

Noise performance of microwave humidity sounders over their lifetime

Abstract. The microwave humidity sounders Special Sensor Microwave Water Vapor Profiler (SSMT-2), Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) to date have been providing data records for 25 years. So far, the data records lack uncertainty information essential for constructing consistent long time data series. In this study, we assess the quality of the recorded data with respect to the uncertainty caused by noise. We calculate the noise on the raw calibration counts from the deep space views (DSVs) of the instrument and the noise equivalent differential temperature (NEΔT) as a measure for the radiometer sensitivity. For this purpose, we use the Allan deviation that is not biased from an underlying varying mean of the data and that has been suggested only recently for application in atmospheric remote sensing. Moreover, we use the bias function related to the Allan deviation to infer the underlying spectrum of the noise. As examples, we investigate the noise spectrum in flight for some instruments. For the assessment of the noise evolution in time, we provide a descriptive and graphical overview of the calculated NEΔT over the life span of each instrument and channel. This overview can serve as an easily accessible information for users interested in the noise performance of a specific instrument, channel and time. Within the time evolution of the noise, we identify periods of instrumental degradation, which manifest themselves in an increasing NEΔT, and periods of erratic behaviour, which show sudden increases of NEΔT interrupting the overall smooth evolution of the noise. From this assessment and subsequent exclusion of the aforementioned periods, we present a chart showing available data records with NEΔT &lt; 1 K. Due to overlapping life spans of the instruments, these reduced data records still cover without gaps the time since 1994 and may therefore serve as a first step for constructing long time series. Our method for count noise estimation, that has been used in this study, will be used in the data processing to provide input values for the uncertainty propagation in the generation of a new set of Fundamental Climate Data Records (FCDRs) that are currently produced in the project Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO).

Hyperspectral Image Segmentation via Frequency-Based Similarity for Mixed Noise Estimation

Accurate approximation of the signal-independent (SI) and signal-dependent (SD) mixed noise from hyperspectral (HS) images is a critical task for many image processing applications where the detection of homogeneous regions plays a key role. Most of the conventional methods empirically divide images into rectangular blocks and then select the homogeneous ones, but it might result in erroneous homogeneity detection, especially for highly textured HS images. To address this challenge, a superpixel segmentation algorithm is proposed in this paper, which can decompose a noisy HS image into patches that adhere to the local structures and hence persist in homogeneous characteristic. A novel spectral similarity measure is defined in the frequency domain to make the superpixel segmentation algorithm more robust to the mixed noise. Combined with an improved scatter-plot-based homogeneous superpixel selection and a multiple linear regression-based noise parameter calculation, our method can accurately estimate SD and SI noise variances from HS images with different noise conditions and various image complexities. We evaluate the proposed method with both synthetic and real Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) HS images. Experimental results demonstrate that the proposed noise estimation method outperforms the state-of-the-art methods.

Pilot-Based Phase Noise, IQ Mismatch, and Channel Distortion Estimation for PDM CO-OFDM System

In this letter, a pilot-based method is proposed to suppress three non-ideal factors, including channel distortion, transmitter in-phase and quadrature (IQ) mismatch, and laser phase noise for polarization division multiplexed coherent optical orthogonal frequency division multiplexing systems. Compared with the conventional phase noise and IQ mismatch estimation methods which only consider one of the two non-ideal factors, we deduce a novel mathematical model considering both and compensate them with special designed pilots: the channel distortion and IQ mismatch are compensate by the block-type pilot while the comb-type pilot is used to estimate phase noise. The simulation results proved that our method can not only better compensate the non-idealities factors, but also can provide high speed transmission with 120 Gb/s over 400-km transmission compared with the conventional methods.

Expose noise level inconsistency incorporating the inhomogeneity scoring strategy

Estimating variances in noise is of key importance in many image processing applications, such as filtering, enhancement, quality assessment, and detecting forgery. For the existing detection methods that are based on inconsistencies in noise, the conventional approach is to estimate the noise variance of each region first and then identify the regions with extremely higher or lower variance as splicing regions. However, due to the impossibility of completely separating image noise and inherent texture, inevitably, each estimate is overestimated, especially for regions that have more complex textures. In this paper, we consider the issue that the estimation of the noise of each region frequently is inaccurate due to the complexity of the texture of the region. Based on this consideration and motivated by the scoring strategy-based, object-proposal technique, an approach that incorporates the inhomogeneity scoring strategy is proposed to provide a more convincing result to expose image-splicing manipulations. Specifically, first, the image is segmented into small patches, and the noise variance of each patch is computed by using the kurtosis concentration-based pixel-level noise estimation method. Then, the inhomogeneity score is computed using the spectral residual-based saliency measurement method. After using a linear equation fitting based on the estimated sample of variance and the inhomogeneity score of each patch, the suspicious region can be identified by seeking the conjunct patches that are out of the linear constraint. The experimental results demonstrated the efficacy and robustness of the proposed method.

No reference noise estimation in digital images using random conditional selection and sampling theory

An accurate quantitative noise estimate is required in many image/video processing applications like denoising, computer vision, pattern recognition and tracking. But blind and accurate estimation of noise in an unknown image is a challenging task and hence is an open area of research. We propose the first elegant and novel blind noise estimation method based on random image tile selection and statistical sampling theory for estimating standard deviation of zero mean Gaussian and speckle noise in digital images. Randomly selected samples, i.e., pixels with $$3\times 3$$ neighborhood, are checked for availability of edges in the tile. If there is an edge in the tile at more than one neighboring pixel, the tile is excluded. Only non-edge tiles are used for estimation of noise in the tile and subsequently in the image using the concepts of statistical sampling theory. Finally, we propose a supervised curve fitting approach using the proposed noise estimation model for more accurate estimation of standard deviation of the two types of noise. The proposed technique is computationally efficient as it is a selective random sample-based spatial domain technique. Benchmarking with other contemporary techniques published so far shows that the proposed technique clearly outperforms the others by at least 5% improved noise estimates, over a very wide range of noise.

Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM

Phase noise correction is crucial to exploit full advantage of orthogonal frequency division multiplexing (OFDM) in modern high-data-rate communications. OFDM channel estimation with simultaneous phase noise compensation has therefore drawn much attention and stimulated continuing efforts. Existing methods, however, either have not taken into account the fundamental properties of phase noise or are only able to provide estimates of limited applicability owing to considerable computational complexity. In this paper, we have reformulated the joint estimation problem in the time domain as opposed to existing frequency-domain approaches, which enables us to develop much more efficient algorithms using the majorization-minimization technique. In addition, we propose a method based on dimensionality reduction and the Bayesian Information Criterion (BIC) that can adapt to various phase noise levels and accomplish much lower mean squared error than the benchmarks without incurring much additional computational cost. Several numerical examples with phase noise generated by free-running oscillators or phase-locked loops demonstrate that our proposed algorithms outperform existing methods with respect to both computational efficiency and mean squared error within a large range of signal-to-noise ratios.

Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy.

Natural image noise level estimation based on local statistics for blind noise reduction

This study proposes an automatic noise estimation method based on local statistics for additive white Gaussian noise. Noise estimation is an important process in digital imaging systems. For example, the performance of an image denoising algorithm can be significantly degraded because of poor noise level estimation. Most of the literature on the subject tends to use the true noise level of a noisy image when suppressing noise artifacts. Moreover, even with the given true noise level, these denoising techniques still cannot attain the best result, particularly for images with complicated details. In this study, a patch-based estimation technique is used to estimate for noise level and applies it to the proposed blind image denoising algorithm. Our approach includes selecting low-rank sub-image with removing high-frequency components from the contaminated image. This selection is according to the gradients of patches with the same statistics. Consequently, we need to estimate the noise level from the selected patches using principal component analysis (PCA). For blind denoising applications, the proposed denoising algorithm integrates the undecimated wavelet-based denoising algorithms and PCA to develop the subjective and objective qualities of the observed image, which result from filtering processes. Experiment results depict that the suggested algorithm performs efficiently over a wide range of visual contents and noise conditions, as well as in additive noise. Associated with different conventional noise estimators, the proposed algorithm yields the best performance, higher-quality images, and faster running speed.

Phase Noise Estimation and Suppression for PDM CO-OFDM/OQAM Systems

Phase noise induced common phase error and inter-carrier-interference are serious impairments for coherent optical orthogonal frequency-division multiplexing (CO-OFDM) systems. As have been discussed in previous publications, extra signal-to-noise-ratio (SNR) penalty is required to maintain the system performance under the interference of laser phase noise. The SNR penalty is proportional to the laser linewidth, the time duration of an OFDM symbol, and the system SNR. Therefore, the phase noise induced interference remains the bottleneck for CO-OFDM systems, especially for those systems employing high-order modulation formats, or with long fast Fourier transform window. Thanks to the usage of specially designed filter banks, CO-OFDM/offset quadrature amplitude modulation (CO-OFDM/OQAM) systems demonstrate lower out-of-band leakage and higher spectral efficiency compared with conventional CO-OFDM systems. Recently, both theoretical studies and experimental demonstrations for CO-OFDM/OQAM systems have been widely reported. With the relaxation of orthogonal condition from the complex field to the real field, phase noised induced interference in CO-OFDM/OQAM systems is more complicated than that in conventional CO-OFDM systems. To our best knowledge, there have been no studies on the phase noise estimation and suppression methods for CO-OFDM/OQAM systems. In this paper, we systematically study and analyze phase noise induced interference for CO-OFDM/OQAM systems. Based on the theoretical analysis and the concept of orthogonal basis expansion, we develop a time-domain phase noise estimation and suppression method for polarization-division-multiplexed CO-OFDM/OQAM systems. As shown in numerical Montel Carlo simulations, the proposed method can improve the system robustness against phase noise effectively.