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Journal articles on the topic 'Gaussian noise'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Shi, Yao-Wu, Chen Wang, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi та De-Min Wang. "1/f spectrum estimation based on α-stable distribution in colored Gaussian noise environments". Journal of Low Frequency Noise, Vibration and Active Control 38, № 1 (2018): 18–35. http://dx.doi.org/10.1177/1461348418813291.

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The main goal of this paper is to suppress the effect of unavoidable colored Gaussian noise on declining accuracy of transistor 1/f spectrum estimation. Transistor noises are measured by a nondestructive cross-spectrum measurement method, which is first to amplify the voltage signals through ultra-low noise amplifiers, then input the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output 1/f noise can be characterized more accurately as non-Gaussian α-stable distr

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2

R, Gayathri. "EFFICIENT NON-LOCAL AVERAGING ALGORITHM FOR MEDICAL IMAGES FOR IMPROVED VISUAL QUALITY." ICTACT Journal on Image and Video Processing 11, no. 2 (2020): 2306–9. https://doi.org/10.21917/ijivp.2020.0327.

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Image can be distorted by various ways including sensor inadequacy, transmission error, different noise factors and motion blurring. For controlling and maintaining the visual quality level of the image to be very high, it is very important to improve the image acquisition, image storage and image transmission, etc. Achieving high Peak Signal to Noise Ratio (PSNR) is essential goal of image restoration. This involves removing noises present in the image. Non-Local Means algorithm combined with Laplacian of Gaussian filter finds better results and produces good PSNR against impulse noise as wel

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3

Qin, Feng Qing, Li Hong Zhu, Li Lan Cao, and Wa Nan Yang. "Gaussian Noised Single-Image Super Resolution Reconstruction." Applied Mechanics and Materials 457-458 (October 2013): 1032–36. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1032.

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A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is r

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4

Li, Yongsong, Zhengzhou Li, Kai Wei, Weiqi Xiong, Jiangpeng Yu, and Bo Qi. "Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation." Sensors 19, no. 2 (2019): 339. http://dx.doi.org/10.3390/s19020339.

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Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks

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YANG, ZHIHUI, and JINQIAO DUAN. "AN INTERMEDIATE REGIME FOR EXIT PHENOMENA DRIVEN BY NON-GAUSSIAN LÉVY NOISES." Stochastics and Dynamics 08, no. 03 (2008): 583–91. http://dx.doi.org/10.1142/s0219493708002469.

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A dynamical system driven by non-Gaussian Lévy noises of small intensity is considered. The first exit time of solution orbits from a bounded neighborhood of an attracting equilibrium state is estimated. For a class of non-Gaussian Lévy noises, it is shown that the mean exit time is asymptotically faster than exponential (the well-known Gaussian Brownian noise case) but slower than polynomial (the stable Lévy noise case), in terms of the reciprocal of the small noise intensity.

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Choudhary, Pandava Himanshu, and T. Satya Savithri. "VLSI Architecture for Decision Tree Based Noise Detector and Gaussian Filter for Noise Removal." Journal of VLSI Design and Signal Processing 11, no. 2 (2025): 1–19. https://doi.org/10.46610/jovdsp.2025.v11i02.001.

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This paper focuses on noise removal using a hybrid approach that integrates a decision-tree-based noise detector with a Gaussian filter to reduce Gaussian noise while preserving important image details. Gaussian noise, characterized by random intensity variations, often leads to a loss in visual quality. Conventional filtering applies uniform smoothing, which can cause blurring of edges and textures. To overcome this, a decision tree composed of three modules uniformity analyzer, edge transition detector, and neighborhood correlation evaluator is used to accurately identify noisy pixels. Only

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Shi, Gui Cun, and Fei Xing Wang. "Mixed Noise Image De-Noising Based on EM Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4734–41. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4734.

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Obtaining high quality images is very important in many areas of applied sciences, but images are usually polluted by noise in the process of generation, transmission and acquisition. In recent years, wavelet analysis achieves significant results in the field of image de-noising. However, most of the studies of noise-induced phenomena assume that the noise source is Gaussian. The use of mixed Gaussian and impulse noise is rare, mainly because of the difficulties in handling them. In the process of image de-noising, the noise model’s parameter estimation is a key issue, because the accuracy of

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8

Jain, Anshika, and Maya Ingle. "PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY." BSSS journal of computer 12, no. 1 (2021): 1–10. http://dx.doi.org/10.51767/jc1201.

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Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz.

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9

Kang, Hyekyoung, Chanrok Park, and Hyungjin Yang. "Evaluation of Denoising Performance of ResNet Deep Learning Model for Ultrasound Images Corresponding to Two Frequency Parameters." Bioengineering 11, no. 7 (2024): 723. http://dx.doi.org/10.3390/bioengineering11070723.

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Ultrasound imaging is widely used for accurate diagnosis due to its noninvasive nature and the absence of radiation exposure, which is achieved by controlling the scan frequency. In addition, Gaussian and speckle noises degrade image quality. To address this issue, filtering techniques are typically used in the spatial domain. Recently, deep learning models have been increasingly applied in the field of medical imaging. In this study, we evaluated the effectiveness of a convolutional neural network-based residual network (ResNet) deep learning model for noise reduction when Gaussian and speckl

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Zhou, Yuqian, Jianbo Jiao, Haibin Huang, Jue Wang, and Thomas Huang. "Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10085–86. http://dx.doi.org/10.1609/aaai.v33i01.330110085.

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Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches.

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Ahmed, Abdalla G. M., Jing Ren, and Peter Wonka. "Gaussian Blue Noise." ACM Transactions on Graphics 41, no. 6 (2022): 1–15. http://dx.doi.org/10.1145/3550454.3555519.

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Among the various approaches for producing point distributions with blue noise spectrum, we argue for an optimization framework using Gaussian kernels. We show that with a wise selection of optimization parameters, this approach attains unprecedented quality, provably surpassing the current state of the art attained by the optimal transport (BNOT) approach. Further, we show that our algorithm scales smoothly and feasibly to high dimensions while maintaining the same quality, realizing unprecedented high-quality high-dimensional blue noise sets. Finally, we show an extension to adaptive samplin

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Guo, Yong-Feng, Ya-Jun Shen, Bei Xi, and Jian-Guo Tan. "Colored correlated multiplicative and additive Gaussian colored noises-induced transition of a piecewise nonlinear bistable model." Modern Physics Letters B 31, no. 28 (2017): 1750256. http://dx.doi.org/10.1142/s0217984917502566.

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In this paper, we investigate the steady-state properties of a piecewise nonlinear bistable model driven by multiplicative and additive Gaussian colored noises with colored cross-correlation. Using the unified colored noise approximation, we derive the analytical expression of the steady-state probability density (SPD) function. Then the effects of colored correlated Gaussian colored noises on SPD are presented. According to the research results, it is found that there appear some new nonlinear phenomena in this system. The multiplicative colored noise intensity, the additive colored noise int

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Omkar Lakshmi Jagan, B., and S. Koteswara Rao. "Underwater surveillance in non-Gaussian noisy environment." Measurement and Control 53, no. 1-2 (2020): 250–61. http://dx.doi.org/10.1177/0020294019877515.

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The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly through

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Xing, Mengdi, and Guorong Gao. "An efficient method to remove mixed Gaussian and random-valued impulse noise." PLOS ONE 17, no. 3 (2022): e0264793. http://dx.doi.org/10.1371/journal.pone.0264793.

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Mixed Gaussian and Random-valued impulse noise (RVIN) removal is still a big challenge in the field of image denoising. Existing denoising algorithms have defects in denoising performance and computational complexity. Based on the improved “detecting then filtering” strategy and the idea of inpainting, this paper proposes an efficient method to remove mixed Gaussian and RVIN. The proposed algorithm contains two phases: noise classification and noise removal. The noise classifier is based on Adaptive center-weighted median filter (ACWMF), three-sigma rule and extreme value processing. Different

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Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi та De-Min Wang. "α-spectrum estimation for 1/f processes in noisy environments". Noise & Vibration Worldwide 50, № 2 (2019): 46–55. http://dx.doi.org/10.1177/0957456519827937.

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In the past, 1/ f noise was regarded as a stochastic process that accords with Gaussian distribution. According to our studies, the output transistor 1/ f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear S αS processes and propose a samples normalized cross-correlations–based α-spectrum method effective in noisy environments. Simulation results and diodes noise spectrum estimation results exhibit good performance.

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Yu, Jingning. "Based on Gaussian filter to improve the effect of the images in Gaussian noise and pepper noise." Journal of Physics: Conference Series 2580, no. 1 (2023): 012062. http://dx.doi.org/10.1088/1742-6596/2580/1/012062.

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Abstract Gaussian filter is one of the important research topics. Researchers find that Gaussian filter can suppress noise, but there is still a gap in the completeness of Gaussian filter for denoising. Therefore, the author optimized the Gaussian filter to achieve better filtering effect. By collecting SNR and PSNR data and comparing and analysing different noise types of the data under the same condition, the author explored the denoising effect of Gaussian filter method on Gaussian noise and Salt and pepper noise, and improved the Gaussian filter method. According to the comparison of SNR a

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17

Wang, Chen, Yao-Wu Shi, Lan-Xiang Zhu, Li-Fei Deng, Yi-Ran Shi, and De-Min Wang. "Auto-regressive moving average parameter estimation for 1/f process under colored Gaussian noise background." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261986743. http://dx.doi.org/10.1177/1748302619867439.

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Current algorithms for estimating auto-regressive moving average parameters of transistor 1/f process are usually under noiseless background. Transistor noises are measured by a non-destructive cross-spectrum measurement technique, with transistor noise first passing through dual-channel ultra-low noise amplifiers, then inputting the weak signals into data acquisition card. The data acquisition card collects the voltage signals and outputs the amplified noise for further analysis. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-s

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18

Kumar, Nalin, and M. Nachamai. "Noise Removal and Filtering Techniques used in Medical Images." Oriental journal of computer science and technology 10, no. 1 (2017): 103–13. http://dx.doi.org/10.13005/ojcst/10.01.14.

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Noise removal techniques have become an essential practice in medical imaging application for the study of anatomical structure and image processing of MRI medical images. To report these issues many de-noising algorithm has been developed like Weiner filter, Gaussian filter, median filter etc. In this research work is done with only three of the above filters which are already mentioned were successfully used in medical imaging. The most commonly affected noises in medical MRI image are Salt and Pepper, Speckle, Gaussian and Poisson noise. The medical images taken for comparison include MRI i

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19

Mesuga, Reymond, Cloyd Raymond Pernes, Luther Villacruz, and Mark Anthony Burgonio. "INVESTIGATION ON THE EFFECTS OF RADIOGRAPHIC IMAGE QUALITY ATTRIBUTES ON THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS (CNNS) IN DETECTING COVID-19." PUP Journal of Science and Technology 14, no. 1 (2024): 15–35. https://doi.org/10.70922/zt7bfe34.

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Radiographic image quality is one of the factors that impacts professionals’ decisions when diagnosing lung diseases using X-ray images. Hence, poor radiographic image quality could result in a misleading diagnosis affecting the person being investigated. This is true in human vision, as well as the computer vision. This study investigated the effects of different radiographic image quality attributes (i.e., contrast, Gaussian blur, Gaussian noise, and salt-and-pepper noise) on the performance of various Convolutional Neural Networks (CNNs) models. We use COVID-19 x-ray data as an initiative t

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20

Volovach, V. I., S. V. Yermolova, and Ya V. Yeremina. "View naguszewski signals and radio interference aerospace systems using polyustrovskij models." Informacionno-technologicheskij vestnik, no. 2 (July 30, 2019): 12–19. http://dx.doi.org/10.21499/2409-1650-2019-2-12-19.

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Reviewed and analyzed issues related to Polyustrovsky naguszewski representation of random signals and noises in the PA-dilinjah aerospace systems. It is shown that real signals and non-Gaussian noise can be represented by the corresponding poly-Gaussian processes. The properties of poly-Gaussian random processes are considered and analyzed. The relationship between the parameters of the mixture of signals and noise and their components is analyzed.

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FUENTES, M. A., C. J. TESSONE, H. S. WIO, and R. TORAL. "STOCHASTIC RESONANCE IN BISTABLE AND EXCITABLE SYSTEMS: EFFECT OF NON-GAUSSIAN NOISES." Fluctuation and Noise Letters 03, no. 04 (2003): L365—L371. http://dx.doi.org/10.1142/s0219477503001440.

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We analyze stochastic resonance in systems driven by non-Gaussian noises. For the bistable double well we compare the signal-to-noise ratio resulting from numerical simulations with some quasi-analytical results predicted by a consistent Markovian approximation in the case of a colored non-Gaussian noise. We also study the FitzHugh–Nagumo excitable system in the presence of the same noise. In both systems, we find that, as the noise departs from Gaussian behavior, there is a regime (different for the excitable and the bistable systems) in which there is a notable robustness against noise tunin

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Wang, Kang-Kang, Hui Ye, Ya-Jun Wang, and Ping-Xin Wang. "Time delay and non-Gaussian noise-induced stochastic stability and stochastic resonance for a metapopulation system subjected to a multiplicative periodic signal." Modern Physics Letters B 32, no. 27 (2018): 1850327. http://dx.doi.org/10.1142/s021798491850327x.

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In this paper, the stable state transformation and the effect of the stochastic resonance (SR) for a metapopulation system are investigated, which is disturbed by time delay, the multiplicative non-Gaussian noise, the additive colored Gaussian noise and a multiplicative periodic signal. By use of the fast descent method, the approximation of the unified colored noise and the SR theory, the dynamical behaviors for the steady-state probability function and the SNR are analyzed. It is found that non-Gaussian noise, the colored Gaussian noise and time delay can all reduce the stability of the biol

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Ge, Gen, Zhi Wen Zhu та Jia Xu. "Homoclinic Bifurcation and Chaos in a Noise-InducedΦ6Potential". Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/314328.

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The present paper focuses on the noise-induced chaos in aΦ6oscillator with nonlinear damping. Based on the stochastic Melnikov approach, simple zero points of the stochastic Melnikov integral theoretically mean the necessary condition causing noise-induced chaotic responses in the system. To quantify the noise-induced chaos, the Poincare maps and fractal basin boundaries are constructed to show how the system's motions change from a periodic way to chaos or from random motions to random chaos as the amplitude of the noise increases. Three cases are considered in simulating the system; that is,

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ZHENG, SHENG, CHANGCAI YANG, EMILE A. HENDRIKS, and XIAOJUN WANG. "ADAPTIVE WEIGHTED LEAST SQUARES SVM BASED SNOWING MODEL FOR IMAGE DENOISING." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 06 (2013): 1350043. http://dx.doi.org/10.1142/s0219691313500434.

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We propose a snowing model to iteratively smoothe the various image noises while preserving the important image structures such as edges and lines. Considering the gray image as a digital terrain model, we develop an adaptive weighted least squares support vector machine (LS-SVM) to iteratively estimate the optimal gray surface underlying the noisy image. The LS-SVM works on Gaussian noise while the weighted LS-SVM works on the outliers and non-Gaussian noise. To improve its performance in preserving the directional signal while suppressing the noise, the dominant orientation information of th

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Lingamaiah Kurva, Naga, and S. Varadarajan. "Dual tree complex wavelet transform based image denoising for Kalpana satellite images." International Journal of Engineering & Technology 7, no. 3.29 (2018): 269. http://dx.doi.org/10.14419/ijet.v7i3.29.18810.

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This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and

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Kittisuwan, Pichid. "Low-complexity image denoising based on mixture model and simple form of MMSE estimation." International Journal of Wavelets, Multiresolution and Information Processing 16, no. 06 (2018): 1850052. http://dx.doi.org/10.1142/s0219691318500522.

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In order to enhance efficiency of artificial intelligence (AI) tools such as classification or pattern recognition, it is important to have noise-free data to be processed with AI tools. Therefore, the study of algorithms used for reducing noise is also very significant. In thermal condition, Gaussian noise is important problem in analog circuit and image processing. Therefore, this paper focuses on the study of an algorithm for Gaussian noise reduction. In recent year, Bayesian with wavelet-based methods provides good efficiency in noise reduction and spends short time in processing. In Bayes

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Yamakou, Marius E., and Tat Dat Tran. "Lévy noise-induced self-induced stochastic resonance in a memristive neuron." Nonlinear Dynamics 107, no. 3 (2021): 2847–65. http://dx.doi.org/10.1007/s11071-021-07088-6.

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AbstractAll previous studies on self-induced stochastic resonance (SISR) in neural systems have only considered the idealized Gaussian white noise. Moreover, these studies have ignored one electrophysiological aspect of the nerve cell: its memristive properties. In this paper, first, we show that in the excitable regime, the asymptotic matching of the deterministic timescale and mean escape timescale of an $$\alpha $$ α -stable Lévy process (with value increasing as a power $$\sigma ^{-\alpha }$$ σ - α of the noise amplitude $$\sigma $$ σ , unlike the mean escape timescale of a Gaussian proces

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Cheng, Yuan Yuan, Hai Yan Li, Qi Xiao, Yu Feng Zhang, and Xin Ling Shi. "Gaussian Noise Filter Using Variable Step Time Matrix of PCNN." Applied Mechanics and Materials 48-49 (February 2011): 551–54. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.551.

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A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experi

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Mo, Yang, Yaonan Wang, Hong Yang, Badong Chen, Hui Li, and Zhihong Jiang. "Generalized Maximum Correntropy Kalman Filter for Target Tracking in TianGong-2 Space Laboratory." Space: Science & Technology 2022 (April 6, 2022): 1–15. http://dx.doi.org/10.34133/2022/9796015.

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Target tracking plays an important role in the construction, operation, and maintenance of the space station by the robot, which puts forward high requirements on the accuracy of target tracking. However, the special space environment may cause complex non-Gaussian noise in target tracking data. And the performance of traditional Kalman Filter will deteriorate seriously when the error signals are non-Gaussian, which may lead to mission failure. In the paper, a novel Kalman Filter algorithm with Generalized Maximum Correntropy Criterion (GMCKF) is proposed to improve the tracking accuracy with

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Naveed, Khuram, Shoaib Ehsan, Klaus D. McDonald-Maier, and Naveed Ur Rehman. "A Multiscale Denoising Framework Using Detection Theory with Application to Images from CMOS/CCD Sensors." Sensors 19, no. 1 (2019): 206. http://dx.doi.org/10.3390/s19010206.

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Output from imaging sensors based on CMOS and CCD devices is prone to noise due to inherent electronic fluctuations and low photon count. The resulting noise in the acquired image could be effectively modelled as signal-dependent Poisson noise or as a mixture of Poisson and Gaussian noise. To that end, we propose a generalized framework based on detection theory and hypothesis testing coupled with the variance stability transformation (VST) for Poisson or Poisson–Gaussian denoising. VST transforms signal-dependent Poisson noise to a signal independent Gaussian noise with stable variance. Subse

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Patil*, Rajesh, and Surendra Bhosale. "Multi-Modal Medical Image Denoising using Wavelets: A Comparative Study." Biomedical and Pharmacology Journal 16, no. 4 (2023): 2271–81. http://dx.doi.org/10.13005/bpj/2803.

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In medical image processing Noise removal is an important step for recreating a high-quality image like X-ray, ultrasound, MRI etc. While acquiring, transmitting, and retrieving from storage devices normally images are degraded due to noises like Gaussian, Speckle etc. So, noise must be removed from the images for proper diagnosis. Researchers are still looking for an effective noise reduction means. Wavelet Transform (WT) is considered as a powerful transform method for removal of noise. For denoising of medical images affected by Gaussian noise, various wavelets have been proposed. In this p

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Murad, Thamer Easa, and Yasin Yousif Al-Aboosi. "Statistical properties of underwater acoustic noise in Lake Hamrin, Diyala, Iraq." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 192. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp192-200.

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<p>The greatest challenge in underwater acoustic communication systems is the minimization of underwater impact noise. This article offers an empirical example for determining the statistical properties of underwater acoustic noise in the in Lake Hamrin. The data are measured from various depths reached in Lake Hamrin, Diyala, Iraq. In most communication systems, noise is assumed to be additive as well as Gaussian. Underwater acoustic noise (UWAN) isn't only thermal noise, it also includes other components to the UWAN: turbulence, wind and shipping noises. Thus, it should be assumed that

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Murad, Thamer Easa, and Yasin Yousif Al-Aboosi. "Statistical properties of underwater acoustic noise in Lake Hamrin, Diyala, Iraq." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 192–200. https://doi.org/10.11591/ijeecs.v28.i1.pp192-200.

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The greatest challenge in underwater acoustic communication systems is the minimization of underwater impact noise. This article offers an empirical example for determining the statistical properties of underwater acoustic noise in the in Lake Hamrin. The data are measured from various depths reached in Lake Hamrin, Diyala, Iraq. In most communication systems, noise is assumed to be additive as well as Gaussian. Underwater acoustic noise (UWAN) isn't only thermal noise, it also includes other components to the UWAN: turbulence, wind and shipping noises. Thus, it should be assumed that the

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34

Chu, P. L. "Fast Gaussian noise generator." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 10 (1989): 1593–97. http://dx.doi.org/10.1109/29.35399.

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Li, Ming, Xichao Sun, and Xi Xiao. "Revisiting fractional Gaussian noise." Physica A: Statistical Mechanics and its Applications 514 (January 2019): 56–62. http://dx.doi.org/10.1016/j.physa.2018.09.008.

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Prasad, M. Satya, and David C. Joy. "Is SEM Noise Gaussian?" Microscopy and Microanalysis 9, S02 (2003): 982–83. http://dx.doi.org/10.1017/s1431927603444917.

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Filippov, Sergey, and Alena Termanova. "Superior Resilience of Non-Gaussian Entanglement against Local Gaussian Noises." Entropy 25, no. 1 (2022): 75. http://dx.doi.org/10.3390/e25010075.

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Entanglement distribution task encounters a problem of how the initial entangled state should be prepared in order to remain entangled the longest possible time when subjected to local noises. In the realm of continuous-variable states and local Gaussian channels it is tempting to assume that the optimal initial state with the most robust entanglement is Gaussian too; however, this is not the case. Here we prove that specific non-Gaussian two-mode states remain entangled under the effect of deterministic local attenuation or amplification (Gaussian channels with the attenuation factor/power ga

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Thanh, D. N. H., and S. D. Dvoenko. "A DENOISING OF BIOMEDICAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5/W6 (May 18, 2015): 73–78. http://dx.doi.org/10.5194/isprsarchives-xl-5-w6-73-2015.

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Today imaging science has an important development and has many applications in different fields of life. The researched object of imaging science is digital image that can be created by many digital devices. Biomedical image is one of types of digital images. One of the limits of using digital devices to create digital images is noise. Noise reduces the image quality. It appears in almost types of images, including biomedical images too. The type of noise in this case can be considered as combination of Gaussian and Poisson noises. In this paper we propose method to remove noise by using tota

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Qiao, Zhong Tao, Feng Qi Gao, Guang Long Wang, and Liang Liang Chang. "A Denoising Mixed Noise Method Based on Median Filter and Lifting Wavelet Technology." Applied Mechanics and Materials 411-414 (September 2013): 1546–51. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1546.

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In image digitization and transmission, images often suffer contamination inevitably. The noises in images often consist of Gaussian noise and impulse noise. The common denoising algorithms are capable of removing single one of them. In order to remove those two types of noise, a composite algorithm is proposed. Firstly, based on median filter, an impulse noise detection algorithm is used to filter impulse noise. Secondly, adaptive directional lifting wavelet (ADL) and normal lifting wavelet is combined to suppress noise from image signal and protect the texture edge from loss simultaneously.

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Zayed, M. Ramadan. "Optimum Image Filters for Various Types of Noise." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 5 (2018): 2458–64. https://doi.org/10.12928/TELKOMNIKA.v16i5.10508.

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In this paper, the quality performance of several filters in restoration of images corrupted with various types of noise has been examined extensively. In particular, Wiener filter, Gaussian filter, median filter and averaging (mean) filter have been used to reduce Gaussian noise, speckle noise, salt and pepper noise and Poisson noise. Many images have been tested, two of which are shown in this paper. Several percentages of noise corrupting the images have been examined in the simulations. The size of the sliding window is the same in the four filters used, namely 5x5 for all the indicated no

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Zangi, Sultan M., Atta ur Rahman, Zhao-Xo Ji, Hazrat Ali, and Huan-Guo Zhang. "Decoherence Effects in a Three-Level System under Gaussian Process." Symmetry 14, no. 12 (2022): 2480. http://dx.doi.org/10.3390/sym14122480.

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When subjected to a classical fluctuating field characterized by a Gaussian process, we examine the purity and coherence protection in a three-level quantum system. This symmetry of the three-level system is examined when the local random field is investigated further in the noiseless and noisy regimes. In particular, we consider fractional Gaussian, Gaussian, Ornstein–Uhlenbeck, and power law noisy regimes. We show that the destructive nature of the Ornstein–Uhlenbeck noise toward the symmetry of the qutrit to preserve encoded purity and coherence remains large. Our findings suggest that prop

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Purwanti Ningrum, Ika, Agfianto Eko Putra, and Dian Nursantika. "Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 5, no. 3 (2011): 29. http://dx.doi.org/10.22146/ijccs.5209.

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Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise (Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how we

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ARENAS, Jorge, Chiara VALDERRAMA, and Jorge CARDENAS. "Comparing Kurtosis-adjusted weighted levels with other metrics to assess the risk of hearing loss from non-Gaussian noise exposures." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 5 (2024): 6674–81. http://dx.doi.org/10.3397/in_2024_3851.

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Several studies have shown that impulsive noise can cause more damage to hearing than steady-state noise of equal energy. As a result, a large body of research has been devoted to evaluating the hazards of impulsive noise. However, work environments often have varying noise patterns, including Gaussian background noise combined with high-level transient noises contributing to the worker's daily dose. Thus, an energy-based noise metric underestimates the risk of hearing loss unless incorporating a temporal structure correction term. Kurtosis has been reported as an effective adjunct to energy f

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Park, Hyun-Sang. "Gaussian Noise Estimation Using White Noise Test." Journal of Korean Institute of Information Technology 16, no. 4 (2018): 51–56. http://dx.doi.org/10.14801/jkiit.2018.16.4.51.

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Deepa, N. Reddy, and Ravinder Yerram. "Spectrum Sensing in Non-Gaussian Noise." Indian Journal of Science and Technology 14, no. 32 (2021): 2596–606. https://doi.org/10.17485/IJST/v14i32.1034.

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Abstract <strong>Background:</strong> Spectrum sensing is a crucial step to realize the Cognitive Radio technology. The spectrum sensing schemes at low signal-to-noise ratio, noise uncertainty and especially under the background of non-Gaussian noise, provide low detection of the primary user. This results in missed detection or false alarm and increases higher interference to the primary user. <strong>Objectives:</strong> Detection schemes designed for additive Gaussian noise exhibit poor performance in the non-Gaussian environment. This study considers the problem of spectrum

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Cengiz, Enes, Muhammed Mustafa Kelek, Yüksel Oğuz, and Cemal Yılmaz. "Classification of breast cancer with deep learning from noisy images using wavelet transform." Biomedical Engineering / Biomedizinische Technik 67, no. 2 (2022): 143–50. http://dx.doi.org/10.1515/bmt-2021-0163.

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Abstract In this study, breast cancer classification as benign or malignant was made using images obtained by histopathological procedures, one of the medical imaging techniques. First of all, different noise types and several intensities were added to the images in the used data set. Then, the noise in images was removed by applying the Wavelet Transform (WT) process to noisy images. The performance rates in the denoising process were found out by evaluating Peak Signal to Noise Rate (PSNR) values of the images. The Gaussian noise type gave better results than other noise types considering PS

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Fan, Ai Ai, and Guang Long Wang. "A Mixed Denoising Method Based on Median Filter and Lifting Wavelet Technology for Sewage Sensing Signal Treatment." Applied Mechanics and Materials 330 (June 2013): 967–72. http://dx.doi.org/10.4028/www.scientific.net/amm.330.967.

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Digital signals are often contaminated by noise during signal acquisition and transmission for sewage sensing signal treatment such as aeration volume, oxygen content and water transparency etc. Sometimes, noise is a mixed one of gaussian noise and impulse noise. Unfortunately, existing denoising algorithms are often designed for removing single gaussian noise or impulse noise. In this paper, an efficient algorithm for mixed noise removal in signal is proposed, including space impulse noise removal and wavelet Gaussian noise removal. An impulse noise detection algorithm based on median filter

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Chuang, Shang Jen, Chiung Hsing Chen, Chien Chih Kao, and Fang Tsung Liu. "Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network." Advanced Materials Research 189-193 (February 2011): 2042–45. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.2042.

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English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test patte

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Mahsa, Jabbar, Ghorbaniparvar Hamidreza, and Ghorbaniparvar Fatemeh. "Sensitivity of the General Linear Model to Assumptions." International Journal of Innovative Science and Modern Engineering (IJISME) 6, no. 5 (2020): 21–25. https://doi.org/10.35940/ijisme.E1210.036520.

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Non-Gaussian noise often causes in significant performance abatement for systems which are designed using Gaussian assumption. This report challenges the question of General Linear Model with White Gaussian Noise assumption in order to define the sensitivity of the performance of an optimal estimator. Gaussian noise models provide an important role in many signal processing applications. The Laplacian and Uniform signal are two worthy examples of noise that can be compared to the White Gaussian Noise, though the sensitivity which can be compared with any non-Gaussian. White Gaussian Noise has

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Kesrarat, Darun, and Vorapoj Patanavijit. "Experimental Study in Error Vector Magnitude of Bidirectional Confidential with Median Filter on Spatial Domain Optical Flow under Non Gaussian Noise Contamination." ECTI Transactions on Electrical Engineering, Electronics, and Communications 14, no. 2 (2016): 1–10. http://dx.doi.org/10.37936/ecti-eec.2016142.171135.

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In this paper, we focus on the robustness in noise tolerance of spatial domain optical flow and we present a performance study of bidirectional confidential with median filter on spatial domain optical flow (spatial correlation, local based, and global based) under non Gaussian noise where several noise tolerance models on spatial domain optical flow are used in comparison. The experiment results are investigated on robustness under noisy condition by using non Gaussian noise (Poisson Noise, Salt & Pepper noise, and Speckle Noise) in contamination with several standard sequences. The exper

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