Noise Estimation Algorithm Research Articles

An advanced adaptive algorithm driven by online blind noise level estimation for pedestrian positioning

An effective and universal positioning algorithm is essential for a highly reliable and accurate foot-mounted inertial pedestrian navigation system. Algorithm performance is significantly influenced by the accuracy of noise level estimation. This study introduces a novel blind noise estimation algorithm to reduce the impact of uncertain and variable system noise characteristics on positioning accuracy. The algorithm combines a quasi-population growth wavelet transform threshold with an adaptive window length adjustment strategy. Firstly, a quasi-population growth wavelet threshold function is employed to separate motion signals from noise and assess noise levels. Secondly, an adaptive sliding window strategy based on motion-noise correlation optimizing the balance between estimation precision and computational efficiency. These methods enable robust noise level estimation without prior knowledge and achieve real-time adjustments to the system noise matrix in Extended Kalman Filter of the inertial navigation system, thereby significantly enhancing navigation error suppression. The effectiveness of above methodologies has been experimentally validated.

A Robust Noise Estimation Algorithm Based on Redundant Prediction and Local Statistics.

Blind noise level estimation is a key issue in image processing applications that helps improve the visualization and perceptual quality of images. In this paper, we propose an improved block-based noise level estimation algorithm. The proposed algorithm first extracts homogenous patches from a single noisy image using local features, obtaining the covariance matrix eigenvalues of the patches, and constructs dynamic thresholds for outlier discrimination. By analyzing the correlations between scene complexity, noise strength, and other parameters, a nonlinear discriminant coefficient regression model is fitted to accurately predict the number of redundant dimensions and calculate the actual noise level according to the statistical properties of the elements in the redundancy dimension. The experimental results show that the accuracy and robustness of the proposed algorithm are better than those of the existing noise estimation algorithms in various scenes under different noise levels. It performs well overall in terms of performance and execution speed.

High-resolution DOA estimation achieved by a single acoustic vector sensor under anisotropic noise

The acoustic vector sensor (AVS) has a wide application in the direction of arrival (DOA) estimation of sound sources including aircraft, underwater or on-land vehicles, search-and-rescue efforts, etc. The performance of only one acoustic vector sensor can compare favorably with a traditional acoustic array in DOA estimation. The anisotropic noise problem of the vector sensor has had a severe affective on the estimation accuracy and has caused little attention until now. In this paper, an AVS measurement system is designed, and an Anisotropic Sparse Bayesian Learning (SBL) method (the A-SBL method) is proposed to model and estimate the anisotropic noise of the AVS system. A high-resolution DOA estimation method is also introduced based on the noise estimation algorithm. The experimental results exhibit the necessity of modeling the anisotropic noise, and the estimation accuracy of the proposed A-SBL method based on the noise model is verified. The comparison of DOA methods shows the superiority of the proposed method, in which the DOA estimation error is not more than 1°. The proposed method’s utility may be found in applications for localization or tracking of targets with prominent acoustic characteristics when the environment is complicated.

An Optimal Fusion Method of Multiple Inertial Measurement Units Based on Measurement Noise Variance Estimation

At present, most inertial systems generally only contain a single inertial measurement unit (IMU). Considering the low cost and low accuracy of the micro-electromechanical system (MEMS)-IMU, it has attracted much attention to fuse multiple IMUs to improve the accuracy and robustness of the system. In this article, two online noise variance estimators based on second-order-mutual-difference (SOMD) algorithm are proposed for two redundant measurements and multiple redundant measurements, respectively. In addition, the unbiasedness and consistency of the estimators are proved. Using the proposed noise variance estimators, measurement noise variances of each sensor can be estimated in real time when multiple IMUs exist. Based on the estimated noise variance of each sensor, the weighted least squares (WLS) estimation method is used to generate the optimal virtual IMU (VIMU) in the observation domain. Finally, comparative simulations and the real-world experiment were conducted to evaluate the proposed online noise estimation algorithm. The simulation results demonstrate its superiority compared with other noise variance estimation methods, and the real-world experiment results show the effectiveness of the IMU fusion method based on the proposed noise variance estimation algorithm.

Generative Recorrupted-to-Recorrupted: An Unsupervised Image Denoising Network for Arbitrary Noise Distribution

With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising.

245Gb/s P2MP Mobile Fronthaul Downstream Transmission Using Code-Division Multiplexing and Self-Homodyne Coherent Technologies

We have proposed and experimentally demonstrated a high-speed and point-to-multipoint (P2MP) mobile fronthaul (MFH) downstream system based on the code-division multiplexing (CDM) and self-homodyne coherent (SHC) technologies. We find that compared with the traditional 16-ary quadrature amplitude modulation (16-QAM) signal transmission, the change of phase noise between two demultiplexed symbols is much larger in the CDM-16QAM signal transmission because it is the accumulation of phase noise change of n multiplexed symbols. Therefore, a low-complexity pilot sequence-assisted phase noise estimation algorithm is proposed and designed to compensate for the phase noise caused by fiber mismatch length, which makes it possible to use a low-cost distributed feedback (DFB) laser. Besides, the spread signal's distribution in the frequency domain of each code sequence is different, leading to different transmission performances between different remote radio units (RRUs). A power optimization scheme is proposed to balance the transmission performances of different RRUs by multiplying each channel's signal by a normalized weight factor at the transmitter. The experimental results show that the proposed system could successfully support the transmission of 245 Gb/s 16QAM signal from baseband unit (BBU) to 7 RRUs (each RRU could have 2 antennas to transmit signals at 2 polarizations) over 10 km standard single-mode fiber (SSMF). The measured worst bit error rate (BER) value of 14 channels is 1.77e-3 which is below the hard-decision forward error correction (HD-FEC) threshold (3.8e-3).

A Single-Image Noise Estimation Algorithm Based on Pixel-Level Low-Rank Low-Texture Patch and Principal Component Analysis.

Noise level is an important parameter for image denoising in many image-processing applications. We propose a noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component analysis for white Gaussian noise. First, an adaptive clustering algorithm, based on a dichotomy merge, adaptive pixel-level low-rank matrix construction method and a gradient covariance low-texture subblock selection method, is proposed to construct a pixel-level low-rank, low-texture subblock matrix. The adaptive clustering algorithm can improve the low-rank property of the constructed matrix and reduce the content of the image information in the eigenvalues of the matrix. Then, an eigenvalue selection method is proposed to eliminate matrix eigenvalues representing the image to avoid an inaccurate estimation of the noise level caused by using the minimum eigenvalue. The experimental results show that, compared with existing state-of-the-art methods, our proposed algorithm has, in most cases, the highest accuracy and robustness of noise level estimation for various scenarios with different noise levels, especially when the noise is high.

Low complexity blind detection in OFDM systems with phase noise

Phase noise is one of the major impairments that degrade the performance of orthogonal frequency division multiplexing (OFDM) systems. When an OFDM system works at high-order modulation or high carrier frequency, phase noise's impairments cannot be ignored. Existing phase noise estimation algorithms either require a large number of pilots or have high computation complexity. In this paper, we propose a low complexity blind data detector based on the maximum a posteriori (MAP) criterion. With prior knowledge of slow time-varying property of phase noise, a simplified iterative phase noise estimation and data detection algorithm is developed. Compared to existing algorithms, the proposed algorithm does not require pilot data transmission, and achieves satisfactory performance with low computation complexity. Simulation results validate the effectiveness of proposed algorithm.

Modified Bi-SVD and Modified Gradient Algorithms for Noise Subspace Estimation with Full Rank Update for Blind CFO Estimator in OFDM Systems

This paper proposes a modified Gradient algorithm and a modified Bi-SVD algorithm for the estimation of noise subspace and their utility in blind CFO estimation. The proposed modified Bi-SVD and modified Gradient algorithms have the dual advantageous features of not only the improved of noise subspace estimation but also the reduced computational complexities. The results obtained through the modified Gradient and modified Bi-SVD algorithms show excellent agreement with the results of the direct SVD technique requiring relatively more computational complexity. Another important contribution of this paper is the adoption of full-rank updated inverse autocorrelation matrix Rinv into the proposed noise subspace estimation algorithms. The improved noise estimation algorithms have been eventually used to aid the MUSIC algorithm to facilitate improved estimation of CFO required in OFDM systems. This paper substantiates the envisaged improved estimation accuracy of CFO through simulation results derived through proposed algorithms relative to conventional SVD.

Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation

The tightly coupled navigation system is commonly used in UAV products and land vehicles. It adopts the Kalman filter to combine raw satellite observations, including the pseudorange, pseudorange rate and Doppler frequency, with the inertial measurements to achieve high navigational accuracy in GNSS-challenged environments. The accurate estimation of measurement noise covariance can ensure the quick convergence of the Kalman filter and the accuracy of the navigation results. Existing tightly coupled integrated navigation systems employ either constant noise covariance or simple noise covariance updating methods, which cannot accurately reflect the dynamic measurement noises. In this article, we propose an adaptive measurement noise estimation algorithm using a transformer and residual denoising autoencoder (RDAE), which can dynamically estimate the covariance of measurement noise. The residual module is used to solve the gradient degradation problem. The DAE is adopted to learn the essential characteristics from the noisy ephemeris data. By introducing the attention mechanism, the transformer can effectively learn the time and space dependency of long-term ephemeris data, and thus dynamically adjusts the noise covariance with the predicted factors. Extensive experimental results demonstrate that our method can achieve sub-meter positioning accuracy in the outdoor open environment. In a GNSS-degraded environment, our proposed method can still obtain about 3 m positioning accuracy. Another test on a new dataset also confirms that our proposed method has reasonable robustness and adaptability.

Mixed Noise Estimation for Hyperspectral Image Based on Multiple Bands Prediction

The noise in hyperspectral images (HSIs) is mixed with signal-independent (SI) thermal noise and signal-dependent (SD) photon noise. In this letter, a mixed noise estimation algorithm is proposed to estimate the noise level of SI and SD noise, respectively. The proposed algorithm removes the spectral correlation of the HSI by multiple linear regression (MLR) on several neighboring bands. According to the relationship between the prediction coefficients by MLR and the local statistics, an overdetermined equation system with respect to the variances of the SI and SD noise is established. Then the estimated noise variances are solved by the least-squares (LS) method. To ensure the accuracy of local statistics, regional clustering-based spatial preprocessing (RCSP) for HSI superpixel segmentation is employed to detect the homogeneous region. Experimental results on the simulated data confirm considerable improvements, and the real-life data experiment is implemented to validate the performance of the proposed algorithm.

Microphone array speech enhancement based on optimized IMCRA

Microphone array speech enhancement algorithm uses temporal and spatial informa- tion to improve the performance of speech noise reduction significantly. By combining noise estimation algorithm with microphone array speech enhancement, the accuracy of noise estimation is improved, and the computation is reduced. In traditional noise es- timation algorithms, the noise power spectrum is not updated in the presence of speech, which leads to the delay and deviation of noise spectrum estimation. An optimized im- proved minimum controlled recursion average speech enhancement algorithm, based on a microphone matrix is proposed in this paper. It consists of three parts. The first part is the preprocessing, divided into two branches: the upper branch enhances the speech signal, and the lower branch gets the noise. The second part is the optimized improved minimum controlled recursive averaging. The noise power spectrum is updated not only in the non-speech segments but also in the speech segments. Fi- nally, according to the estimated noise power spectrum, the minimum mean-square error log-spectral amplitude algorithm is used to enhance speech. Testing data are from TIMIT and Noisex-92 databases. Short-time objective intelligibility and seg- mental signal-to-noise ratio are chosen as evaluation metrics. Experimental results show that the proposed speech enhancement algorithm can improve the segmental signal-to-noise ratio and short-time objective intelligibility for various noise types at different signal-to-noise ratio levels.

Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Robust estimation of noise strength in Magnetic Resonance Images (MRIs) is a task of great importance due to its extensive applications in image post-processing techniques. Many noise estimation algorithms have been proposed in the past to retrieve noise characteristics of the magnitude image. These algorithms rely on either the spatial or transform domain information of the image. In this research article, we propose a noise estimation algorithm that utilizes a hybrid Discrete Wavelet Transform (DWT) and edge information suppression based algorithm to estimate the strength of noise in magnitude MR images. The wavelet coefficients corresponding to spatial domain edges are eliminated using a down-sampled complement of the Sobel edge map. The distribution of average noise energy in spatial and transform domain which follows Parseval’s theorem is utilized for calculating the initial noise estimate. Further, the robustness of the proposed algorithm is enhanced using Feedforward Neural Network. The proposed algorithm is observed to be computationally fast and accurate. Results on synthetic and clinical T1- and T2-w brain MRIs following Gaussian or Rician distribution show better performance than the existing benchmark algorithms over a wide range of input noise.

Robust wheel wear detection for solid carbide grinding under strong noise interference: A new approach based on improved ensemble noise-reconstructed empirical mode decomposition

Wheel wear detection is of great importance to achieve the desired surface quality and productivity in solid carbide grinding, while the Hilbert–Huang transform composed of empirical mode decomposition (EMD) and Hilbert transform is widely utilized in the research of grinding wear detection. However, previous studies suffer from the following drawbacks: (1) the intrinsic mode functions (IMFs) decomposed by the EMD are prone to mode mixing, hampering the extraction of weak wear features; (2) the heavy noises which could submerge the weak fault features are not considered, whereas collecting signals with a low signal-to-noise ratio (SNR) is ineluctable in practical production. Although the ensemble EMD (EEMD) is proposed to handle the first drawback, the additional Gaussian white noise can aggravate the noise interference. In this paper, an improved ensemble noise-reconstructed EMD (IENEMD) is proposed to tackle these drawbacks. The inherent noise hidden in raw signals is extracted by a newly developed noise estimation algorithm and leveraged in the IENEMD to avoid mode mixing and denoise the inherent noise itself. Both repeatable numerical simulations and practical grinding experiments are studied to validate the reliability and feasibility of the proposed IENEMD, while contrastive analyses are performed with the EMD, EEMD, and ENEMD. The results indicate that the proposed IENEMD has superior abilities of mode mixing elimination and feature extraction in processing signals with low SNR. Moreover, based on the combination of the IENEMD, the Hilbert transform, and the one-dimensional convolutional neural networks, a robust and highly accurate wheel wear detection model is established. Results indicate the model can act as a promising tool for the wear detection of grinding wheels.

Investigation and Mitigation of Noise Contributions in a Compact Heterodyne Interferometer

We present a noise estimation and subtraction algorithm capable of increasing the sensitivity of heterodyne laser interferometers by one order of magnitude. The heterodyne interferometer is specially designed for dynamic measurements of a test mass in the application of sub-Hz inertial sensing. A noise floor of / at 100 is achieved after applying our noise subtraction algorithm to a benchtop prototype interferometer that showed a noise level of / at 100 when tested in vacuum at levels of Torr. Based on the previous results, we investigated noise estimation and subtraction techniques of non-linear optical pathlength noise, laser frequency noise, and temperature fluctuations in heterodyne laser interferometers. For each noise source, we identified its contribution and removed it from the measurement by linear fitting or a spectral analysis algorithm. The noise correction algorithm we present in this article can be generally applied to heterodyne laser interferometers.

Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery.

Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed "subjective inflation." Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise (i.e., "variance misperception"). However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics, which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here, we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors-but used them incorrectly-showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results, which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system.

Application Research of Speech Signal Processing Technology Based on Cloud Computing Platform

In recent years, in the context of the rapid development of information technology, artificial intelligence has also developed. People have begun to train machines. Many machines have been able to gradually understand human languagesand perform a series of actions based on language instructions. On this basis, scientific researchers hope that the machine can be more intelligent and humane. In the noise estimation stage, a noise estimation algorithm based on speech detection is used to effectively estimate the noise. Secondly, according to the characteristics of the method of speech noise reduction processing, a method of processing speech noise is realized. Finally, simulation experiments are used to illustrate the effectiveness of the algorithm. Aiming at the shortcomings of traditional speech noise reduction algorithms, improvements were made in adaptive filter estimation. The model's speech noise reduction algorithm was obtained. The cepstrum estimation of speech signals was modified, and the effect of speech enhancement was significantly improved.

Toward a priori noise characterization for real-time edge-aware denoising in fluoroscopic devices

BackgroundLow-dose X-ray images have become increasingly popular in the last decades, due to the need to guarantee the lowest reasonable patient’s exposure. Dose reduction causes a substantial increase of quantum noise, which needs to be suitably suppressed. In particular, real-time denoising is required to support common interventional fluoroscopy procedures. The knowledge of noise statistics provides precious information that helps to improve denoising performances, thus making noise estimation a crucial task for effective denoising strategies. Noise statistics depend on different factors, but are mainly influenced by the X-ray tube settings, which may vary even within the same procedure. This complicates real-time denoising, because noise estimation should be repeated after any changes in tube settings, which would be hardly feasible in practice. This work investigates the feasibility of an a priori characterization of noise for a single fluoroscopic device, which would obviate the need for inferring noise statics prior to each new images acquisition. The noise estimation algorithm used in this study was tested in silico to assess its accuracy and reliability. Then, real sequences were acquired by imaging two different X-ray phantoms via a commercial fluoroscopic device at various X-ray tube settings. Finally, noise estimation was performed to assess the matching of noise statistics inferred from two different sequences, acquired independently in the same operating conditions.ResultsThe noise estimation algorithm proved capable of retrieving noise statistics, regardless of the particular imaged scene, also achieving good results even by using only 10 frames (mean percentage error lower than 2%). The tests performed on the real fluoroscopic sequences confirmed that the estimated noise statistics are independent of the particular informational content of the scene from which they have been inferred, as they turned out to be consistent in sequences of the two different phantoms acquired independently with the same X-ray tube settings.ConclusionsThe encouraging results suggest that an a priori characterization of noise for a single fluoroscopic device is feasible and could improve the actual implementation of real-time denoising strategies that take advantage of noise statistics to improve the trade-off between noise reduction and details preservation.

Signal-Dependent Noise Estimation for a Real-Camera Model via Weight and Shape Constraints

Most computer vision algorithms require parameter adjustment according to the image noise level. Conventionally, the additive white Gaussian noise (AWGN) model is widely used in most noise estimation algorithms; however, this assumption does not hold in the real world where the noise from cameras is more complex, and it is more appropriate to assume the signal-dependent noise (SDN) model. In this paper, we focus on the SDN model while considering the nonlinear radiometric calibration inside an actual camera, and propose an algorithm to efficiently estimate the noise level function (NLF), which is defined as the noise standard deviation with respect to image intensity. First, the input image is divided into overlapping patches, and noise samples are estimated in the linear transform domain. The confidence levels of the noise samples and the prior of the camera response function are then employed as constraints for the recovery of the NLF. Finally, the noise samples and constraints are represented in a convex optimization problem. The experimental results using both real and synthetic noisy images demonstrate the superiority of the proposed method. In addition, the estimated NLFs are incorporated into two well-known denoising schemes, non-local means and BM3D, and shows significant improvements in denoising SDN-polluted images.

Exposing Speech Transsplicing Forgery with Noise Level Inconsistency

Splicing is one of the most common tampering techniques for speech forgery in many forensic scenarios. Some successful approaches have been presented for detecting speech splicing when the splicing segments have different signal-to-noise ratios (SNRs). However, when the SNRs between the spliced segments are close or even same, no effective detection methods have been reported yet. In this study, noise inconsistency between the original speech and the inserted segment from other speech is utilized to detect the splicing trace. First, noise signal of the suspected speech is extracted by a parameter-optimized noise estimation algorithm. Second, the statistical Mel frequency features are extracted from the estimated noise signal. Finally, the spliced region is located by utilizing a change point detection algorithm on the estimated noise signal. The effectiveness of the proposed method is evaluated on a well-designed speech splicing dataset. The comparative experimental results show that the proposed algorithm can achieve better detection performance than other algorithms.