Nonuniform Noise Research Articles

Fast DOA Estimation of IGS Noncircular Signals Under Coprime Arrays

Traditional direction-of-arrival (DOA) estimation methods often encounter challenges such as low accuracy and high computational complexity, particularly in complex signal environments with multiple sources. To address these issues, this paper proposes a fast DOA estimation method for improper Gaussian signaling (IGS) non-circular signals, based on a coprime array; this approach involves extending the array model by leveraging the unique properties of non-circular signals. Subsequently, this study introduces the ANCM algorithm, a method designed for fast DOA estimation of IGS non-circular signals within a non-uniform noise background. The proposed ANCM algorithm is then compared to classical algorithms, through simulations in a non-uniform noise environment. Experimental results demonstrate that the ANCM algorithm significantly outperforms traditional methods in such conditions. Therefore, the development of new, efficient DOA estimation methods holds substantial theoretical and practical value.

A high-resolution method for direction of arrival estimation based on an improved self-attention module.

The high-resolution direction of arrival (DOA) estimation is a prominent research issue in underwater acoustics. The existing high-resolution methods include subspace methods and sparse representation methods. However, the performance of subspace methods suffers from low signal-to-noise ratio (SNR) and limited snapshots conditions, and the computational complexity of sparse representation methods is too high. The neural network methods are emerging high-resolution methods. However, insufficient support for big data is frequently observed in underwater acoustics, and conventional network structures present challenges in further enhancing performance. To address the aforementioned problems, we propose a neural network method based on an improved self-attention module to achieve high accuracy and robust DOA estimation. First, we design a multi-head self-attention module with large-scale convolutional kernels and residual structures to improve the estimated accuracy. Second, we propose an improved input feature to enhance the robustness to non-uniform noise and unequal-intensity targets. The simulations demonstrate that the proposed method exhibits superior angle resolution compared to sparse representation methods under the same simulation conditions. The proposed method demonstrates exceptional accuracy and robustness in DOA estimation under challenging conditions of low SNR, limited snapshots, and unequal-intensity targets. The experimental results further prove the effectiveness of the proposed method.

Self correction technique for amplitude and phase errors of underwater acoustic arrays based on variable decibel Bayesian inference

Abstract Direction of arrival (DOA) estimation of underwater targets is an important research direction in the field of underwater acoustic array signal processing. Due to factors such as assembly errors and component aging, amplitude and phase errors between each channel of the array are common, resulting in distortion of the array flow pattern matrix. At the same time, the non-uniform distribution of noise intensity at the positions of each array element further increases the difficulty of DOA for underwater targets. Based on the characteristics of sparse spatial distribution of underwater targets, this paper uses the flexibility of hyperparametric modeling to construct an array observation signal graph model. On this basis, the effects of amplitude and phase errors and non-uniform noise on the array model are added, and a comprehensive observation model with clear distribution types of amplitude and phase errors, signals, and noise is established. The variational Bayesian inference theory is used to obtain closed solutions of various variables and parameters, so as to achieve joint estimation of target azimuth and error parameters. Simulation results and sea trial data processing results show that the algorithm can achieve amplitude and phase error self correction while completing DOA estimation in the absence of prior information on the number of sources.

Robust Direction-of-Arrival Estimation in the Presence of Outliers and Noise Nonuniformity

In direction-of-arrival (DOA) estimation with sensor arrays, the background noise is usually modeled to be uncorrelated uniform white noise, such that the related algorithms can be greatly simplified by making use of the property of the noise covariance matrix being a diagonal matrix with identical diagonal entries. However, this model can be easily violated by the nonuniformity of sensor noise and the presence of outliers that may arise from unexpected impulsive noise. To tackle this problem, we first introduce an exploratory factor analysis (EFA) model for DOA estimation in nonuniform noise. Then, to deal with the outliers, a generalized extreme Studentized deviate (ESD) test is applied for outlier detection and trimming. Based on the trimmed data matrix, a modified EFA model, which belongs to weighted least-squares (WLS) fitting problems, is presented. Furthermore, a monotonic convergent iterative reweighted least-squares (IRLS) algorithm, called the iterative majorization approach, is introduced to solve the WLS problem. Simulation results show that the proposed algorithm offers improved robustness against nonuniform noise and observation outliers over traditional algorithms.

Coronary Vessel Segmentation in X-ray Angiography Images Using Edge-Based Tracking Method

Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.

Deep learning-based restoration of multi-degraded finger-vein image by non-uniform illumination and noise

The recognition performance deteriorates if degradation factors including blur, noise, and non-uniform illumination exist in the image when acquiring a finger-vein image. Especially, multiple degradation factors can occur when acquiring the finger-vein image, and they require the image restoration. However, previous flow-based model produced lower image quality than the other restoration models, and diffusion-based model had the disadvantage of slow inference speed. Therefore, this study suggests a deep learning-based generative adversarial network for multi-degraded finger-vein image restoration by non-uniform illumination and noise (MFNN-GAN). It considers multiple degradation factors such as non-uniform illumination and noise. Unlike the existing finger-vein image restoration model, MFNN-GAN is capable of adaptive restoration to multiple degradations. Therefore, even if the illumination by near-infrared (NIR) illuminator of finger-vein recognition device is weak or non-uniform, or the consequent captured image is noisy, good recognition performance can be achieved only by our method without replacing the illuminator or camera sensor.The experimental results obtained using finger-vein open datasets, session 1 images from database version 1 of the Hong Kong Polytechnic University finger-image (HKPU-DB) and finger-vein database of SDUMLA-HMT (SDUMLA-HMT-DB)-based degraded databases. The experimental results show that we obtained the lower equal error rate (EER) of finger-vein recognition using MFNN-GAN compared to other state-of-the-art algorithms.

Eigenstructure methods for DOA estimation of circular acoustic vector sensor array with axial angle bias in nonuniform noise

To improve the performance of direction of arrival (DOA) estimation under the coexistence of nonuniform noise and axial angle bias, we propose new DOA estimation methods based on the eigendecomposition of a covariance matrix constructed by the analytic velocity and acoustic pressure. The analytical velocity model can convert axial angle bias into phase error to facilitate its estimation. Meanwhile, the diagonal unloading (DU) technique equalizes the noise power to minimize the effect of noise non-uniformity, and the minimum diagonal element of this covariance matrix can precisely determine the amount of DU. Next, two eigenstructure-based estimation algorithms are developed from the objective function formulated by the orthogonality of the true steering vector and the noise subspace. The first method is to get a quadratic matrix from this objective function and estimate the DOAs by judging its singularity at different spatial angles. In the second method, an optimization problem is formulated to deduce a closed-form solution of the bias weight vector corresponding to the preset spatial angle, and a joint iterative approach further improves the estimation accuracy of the DOAs and axial angle bias parameters. Simulation results are provided to show the superiority of the proposed methods.

Noise Nonuniformity Dependency on Exposure Settings in Computed Tomography

In X-ray computed tomography (CT), noise distribution within images is nonuniform and thought to vary with imaging conditions. This study aimed to evaluate noise nonuniformity by altering specific imaging conditions, such as tube voltage, bow-tie filter (BTF), and phantom size. Using four tube voltages (80, 100, 120, and 135 kV), two BTF types (L and M), and circular water phantoms with diameters of 240, 320, and 400 mm, we employed filtered back projection (FBP) for reconstruction. Noise nonuniformity was assessed by defining six regions of interest (ROI) from the image center to the periphery, and the noise nonuniformity index (NNI) was calculated based on the standard deviation (SD) values within these ROIs. Results showed consistently larger noise SD values in the central region compared to the peripheral region under all imaging conditions, with the maximum NNI reaching 32.1%. Variations in NNI were observed, reaching up to 5.5 points for tube voltage, 7.8 points for BTF, and 8.2 points for phantom size. In conclusion, our quantitative assessment revealed moderate dependence of noise nonuniformity on imaging conditions in CT images.

Source Number Estimation in 2D Uniform Circular and L-Shaped Arrays With Unknown Nonuniform Noise

Source Number Estimation in 2D Uniform Circular and L-Shaped Arrays With Unknown Nonuniform Noise

DoA Estimation of Nonuniform Noise Sources via Off-Grid SBL in a Noncooperative Mode Using a Single RF Link

DoA Estimation of Nonuniform Noise Sources via Off-Grid SBL in a Noncooperative Mode Using a Single RF Link

Retracted: 2D-DOA Estimation for EMVS Array with Nonuniform Noise

Retracted: 2D-DOA Estimation for EMVS Array with Nonuniform Noise

Direction of Arrival Estimation With Gain-Phase Uncertainties in Unknown Nonuniform Noise

This paper considers the problem of direction-of-arrival (DOA) estimation for narrowband source signals under the coexistence of gain-phase uncertainties and nonuniform noise. Firstly, by exploiting the low-rank property of the covariance of signal components, and the diagonal property of noise covariance, an optimization problem with low-rank constraints is formulated to estimate the nonuniform noise covariance matrix, which is then solved efficiently by the alternating direction method of multipliers (ADMM). Secondly, a linear transformation operator is employed to construct a rank reduction (RARE) estimator, and subsequently an initial DOA and gain-phase uncertainties are obtained via one-dimensional (1-D) spectral search. With initial estimates, an augmented DOA estimation is finally achieved by constructing a weighted and structured sparse total least-squares (WSS-TLS) optimization problem, which is solved using the block coordinate descent algorithm. Simulation results show the superior performance of the proposed method, and it achieves better tradeoff between computational complexity and performance, compared to existing methods.

An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.

L-PRNU: Low-Complexity Privacy-Preserving PRNU-Based Camera Attribution Scheme

A personal camera fingerprint can be created from images in social media by using Photo Response Non-Uniformity (PRNU) noise, which is used to identify whether an unknown picture belongs to them. Social media has become ubiquitous in recent years and many of us regularly share photos of our daily lives online. However, due to the ease of creating a PRNU-based camera fingerprint, the privacy leakage problem is taken more seriously. To address this issue, a security scheme based on Boneh–Goh–Nissim (BGN) encryption was proposed in 2021. While effective, the BGN encryption incurs a high run-time computational overhead due to its power computation. Therefore, we devised a new scheme to address this issue, employing polynomial encryption and pixel confusion methods, resulting in a computation time that is over ten times faster than BGN encryption. This eliminates the need to only send critical pixels to a Third-Party Expert in the previous method. Furthermore, our scheme does not require decryption, as polynomial encryption and pixel confusion do not alter the correlation value. Consequently, the scheme we presented surpasses previous methods in both theoretical analysis and experimental performance, being faster and more capable.

Single-Frame Infrared Image Non-Uniformity Correction Based on Wavelet Domain Noise Separation.

In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image quality, and are susceptible to ghosting artifacts from neighboring frames. In response to such challenges, we propose a method for the correction of non-uniformity in single-frame infrared images based on noise separation in the wavelet domain. More specifically, we commence by decomposing the noisy image into distinct frequency components through wavelet transformation. Subsequently, we employ a clustering algorithm to extract high-frequency noise from the vertical components within the wavelet domain, concurrently employing a method of surface fitting to capture low-frequency noise from the approximate components within the wavelet domain. Ultimately, the restored image is obtained by subtracting the combined noise components. The experimental results demonstrate that the proposed method, when applied to simulated noisy images, achieves the optimal levels among seven compared methods in terms of MSE, PSNR, and SSIM metrics. After correction on three sets of real-world test image sequences, the average non-uniformity index is reduced by 75.54%. Moreover, our method does not impose significant computational overhead in the elimination of superimposed noise, which is particularly suitable for applications necessitating stringent requirements in both image quality and processing speed.

Correcting non-independent and non-identically distributed errors with surface codes

A common approach to studying the performance of quantum error correcting codes is to assume independent and identically distributed single-qubit errors. However, the available experimental data shows that realistic errors in modern multi-qubit devices are typically neither independent nor identical across qubits. In this work, we develop and investigate the properties of topological surface codes adapted to a known noise structure by Clifford conjugations. We show that the surface code locally tailored to non-uniform single-qubit noise in conjunction with a scalable matching decoder yields an increase in error thresholds and exponential suppression of sub-threshold failure rates when compared to the standard surface code. Furthermore, we study the behaviour of the tailored surface code under local two-qubit noise and show the role that code degeneracy plays in correcting such noise. The proposed methods do not require additional overhead in terms of the number of qubits or gates and use a standard matching decoder, hence come at no extra cost compared to the standard surface-code error correction.

Expectation-maximization type algorithms for direction of arrival estimation in unknown nonuniform noise

The expectation–maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonuniform noise. In this work, we present several computationally efficient EM-type algorithms, which update the parameter estimates sequentially, for solving both the deterministic and stochastic maximum–likelihood (ML) direction finding problems in unknown nonuniform noise. Specifically, we design a generalized EM (GEM) algorithm and a space-alternating generalized EM (SAGE) algorithm for computing the deterministic ML estimator. Simulation results show that the SAGE algorithm outperforms the GEM algorithm. Moreover, we design two SAGE algorithms for computing the stochastic ML estimator, in which the first updates the DOA estimates simultaneously while the second updates the DOA estimates sequentially. Simulation results show that the second SAGE algorithm outperforms the first one.

DOA Estimation Based on Gridless Fuzzy Active Learning Under Unknown Mutual Coupling and Nonuniform Noise: Experimental Verification

In this paper, we proposed an active learning method for the DOA estimation based on uniform linear array (ULA) antennas with a very low computational complexity. In particular, an adaptive Fuzzy sampling strategy was used to make a model and refine it according to the snapshot data collected by multiple antennas to estimate the DOA angles. The proposed gridless Fuzzy pipeline sequential design zero-forcing (PLSD-ZF) sampling can reduce complexity by dividing the angle space into subsets and using the measurements of the appropriate subsets to obtain more accurate DOA estimates. Our simulation and experiment results demonstrate that the proposed fuzzy PLSD-ZF scheme outperforms other well-known ones in the presence of unknown mutual coupling, non-uniform noise, and data with a mixture distribution.

Low-Complexity DOA Estimation via Synthetic Coprime Polarization Sensitive Array with Reduced Mutual Coupling in Nonuniform Noise

Low-Complexity DOA Estimation via Synthetic Coprime Polarization Sensitive Array with Reduced Mutual Coupling in Nonuniform Noise

Attention based DOA estimation in the presence of unknown nonuniform noise

To solve the problem of performance degradation of the direction-of-arrival (DOA) estimate in non-uniform noise environment, we propose a novel attention mechanism using deep learning technology, namely array covariance attention (ACA). Specifically, to design the ACA, according to the structural characteristics of the covariance matrix, the pooling operation is removed, and one-dimensional convolution kernels are used to aggregate correlation characteristics in two spatial directions. With fully connected layers and non-linear activation layers, the characteristics are then coded into the perceptual attention matrix to improve useful information of the covariance matrix. Furthermore, to achieve a better performance, the integration position of the attention mechanism is also discussed in the network. Finally, a new deep-learning network is created for DOA estimation in the presence of non-uniform noise. The experimental results demonstrate the efficiency and superiority of the proposed network.