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Journal articles on the topic 'Additive Gaussian noise'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

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 (October 10, 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 intensity and the cross-correlation strength between noises can induce the transition. However, the transition cannot be induced by the auto-correlation time of multiplicative and additive Gaussian colored noises as well as the cross-correlation time between noises.

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2

Idel, Martin, and Robert Konig. "On quantum additive Gaussian noise channels." Quantum Information and Computation 17, no. 3&4 (March 2017): 283–302. http://dx.doi.org/10.26421/qic17.3-4-6.

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We give necessary and sufficient conditions for a Gaussian quantum channel to have a dilation involving a passive, i.e., number-preserving unitary. We then establish a normal form of such channels: any passively dilatable channel is the result of applying passive unitaries to the input and output of a Gaussian additive channel. The latter combine the state of the system with that of the environment by means of a multi-mode beamsplitter.

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3

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 (September 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 biological system, and even lead to the population extinction. Inversely, the self-correlation times of two noises can both increase the stability of the population system and be in favor of the population reproduction. As regards the SNR for the metapopulation system induced by the noise terms and time delay, it is discovered that time delay and the correlation time of the multiplicative noise can effectively enhance the SR effect, while the multiplicative noise and the correlation time of the additive noise would all the time suppress the SR phenomena. In addition, the additive noise can effectively motivate the SR effect, but not alter the peak value of the SNR. It is worth noting that the departure parameter from the Gaussian noise plays the diametrical roles in stimulating the SR effect in different cases.

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4

Haynes, Mark S. "Homodyned-K Distribution With Additive Gaussian Noise." IEEE Transactions on Aerospace and Electronic Systems 55, no. 6 (December 2019): 2992–3002. http://dx.doi.org/10.1109/taes.2019.2895711.

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5

Ding, L., H. N. Wang, J. Chen, and Z. H. Guan. "Tracking under additive white Gaussian noise effect." IET Control Theory & Applications 4, no. 11 (November 1, 2010): 2471–78. http://dx.doi.org/10.1049/iet-cta.2009.0449.

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6

CHAPEAU-BLONDEAU, FRANÇOIS, and DAVID ROUSSEAU. "CONSTRUCTIVE ACTION OF ADDITIVE NOISE IN OPTIMAL DETECTION." International Journal of Bifurcation and Chaos 15, no. 09 (September 2005): 2985–94. http://dx.doi.org/10.1142/s0218127405013824.

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The optimal detection of a signal of known form hidden in additive white noise is examined in the framework of stochastic resonance and noise-aided information processing. Conditions are exhibited where the performance in the optimal detection increases when the level of the additive (non-Gaussian bimodal) noise is raised. On the additive signal–noise mixture, when a threshold quantization is performed prior to the optimal detection, another form of improvement by noise can be obtained, with subthreshold signals and Gaussian noise. Optimization of the quantization threshold shows that even in symmetric detection settings, the optimal threshold can be away from the center of symmetry and in subthreshold configuration of the signals. These properties concerning non-Gaussian noise and nonlinear preprocessing in optimal detection, are meaningful to the current exploration of the various modalities and potentialities of stochastic resonance.

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7

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. Our preliminary results demonstrated the effectiveness of the newly-proposed noise model and adaptation strategies.

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8

Ruggeri, G., and S. Mancin. "Quantum Gaussian channels with additive correlated classical noise." Quantum Information and Computation 7, no. 3 (March 2007): 265–72. http://dx.doi.org/10.26421/qic7.3-6.

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We provide a model to study memory effects in quantum Gaussian channels with additive classical noise over an arbitrary number of uses. The correlation among different uses is introduced by contiguous two-mode interactions. Numerical results for few modes are presented. They confirm the possibility to enhance the classical information rate with the aid of entangled inputs, and show a likely asymptotic behavior that should lead to the full capacity of the channel.

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9

Sreedevi, M., and P. Jenoaul. "Additive White Gaussian Noise Removal Using Viterbi Algorithm." Asian Journal of Information Technology 10, no. 3 (March 1, 2011): 119–21. http://dx.doi.org/10.3923/ajit.2011.119.121.

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10

Naseri, Mostafa, and Norman C. Beaulieu. "Fast Simulation of Additive Generalized Gaussian Noise Environments." IEEE Communications Letters 24, no. 8 (August 2020): 1651–54. http://dx.doi.org/10.1109/lcomm.2020.2989246.

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11

Chen, Yuan, Ercan Engin Kuruoglu, and Hing Cheung So. "Optimum linear regression in additive Cauchy–Gaussian noise." Signal Processing 106 (January 2015): 312–18. http://dx.doi.org/10.1016/j.sigpro.2014.07.028.

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12

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 (January 16, 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 can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

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13

Wang, Kang-Kang, Hui Ye, Ya-Jun Wang, and Ping-Xin Wang. "Impact of Time Delay and Non-Gaussian Noise on Stochastic Resonance and Stability for a Stochastic Metapopulation System Driven by a Multiplicative Periodic Signal." Fluctuation and Noise Letters 18, no. 03 (July 16, 2019): 1950017. http://dx.doi.org/10.1142/s0219477519500172.

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In the present paper, the stability of the population system and the phenomena of the stochastic resonance (SR) for a metapopulation system induced by the terms of time delay, the multiplicative non-Gaussian noise, the additive colored Gaussian noise and a multiplicative periodic signal are investigated in detail. By applying the fast descent method, the unified colored noise approximation and the SR theory, the expressions of the steady-state probability function and the SNR are derived. It is shown that multiplicative non-Gaussian noise, the additive Gaussian noise and time delay can all weaken the stability of the population system, and even result in population extinction. Conversely, the two noise correlation times can both strengthen the stability of the biological system and contribute to group survival. In regard to the SNR for the metapopulation system impacted by the noise terms and time delay, it is revealed that the correlation time of the multiplicative noise can improve effectively the SR effect, while time delay would all along restrain the SR phenomena. On the other hand, although the additive noise and its correlation time can stimulate easily the SR effect, they cannot change the maximum of the SNR. In addition, the departure parameter from the Gaussian noise and the multiplicative noise play the opposite roles in motivating the SR effect in different cases.

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14

GOSWAMI, GURUPADA, PRADIP MAJEE, and BIDHAN CHANDRA BAG. "ESCAPE THROUGH A FLUCTUATING ENERGY BARRIER IN THE PRESENCE OF NON-GAUSSIAN NOISE." Fluctuation and Noise Letters 07, no. 02 (June 2007): L151—L161. http://dx.doi.org/10.1142/s0219477507003799.

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In this paper we have studied how barrier crossing dynamics is affected by colored additive non-Gaussian noise if the barrier fluctuates deterministically. Our investigation indicates that resonant activation(RA) is either enhanced or it becomes robust if noise characteristic is deviated from the Gaussian behavior. We find that additive colored non Gaussian noise can induce the RA-like phenomenon. Another interesting observation is that the turnover behavior persists even in presence of barrier fluctuations at finite rate. Finally, it is observed that mean first passage time(MFPT) decreases with increase of non-Gaussian characteristic of the additive colored noise for a given noise strength and noise correlation time and ultimately reaches to a limiting value. The limiting value remains almost the same if the barrier fluctuating frequency is zero or far from the resonant condition. But near the resonant condition the mean first passage time initially decreases and then increases passing through a minimum as the non Gaussian parameter grows.

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15

Xi, Bei, Yong-Feng Guo, Ya-Jun Shen, Jian-Guo Tan, and Ming Liu. "Multiplicative non-Gaussian noise and additive Gaussian white noise induced transition in a piecewise nonlinear model." Chinese Journal of Physics 55, no. 1 (February 2017): 1–9. http://dx.doi.org/10.1016/j.cjph.2016.11.004.

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16

Khoolenjani, Nayereh Bagheri, and Mohammad Hossein Alamatsaz. "Extension of de Bruijn's identity to dependent non-Gaussian noise channels." Journal of Applied Probability 53, no. 2 (June 2016): 360–68. http://dx.doi.org/10.1017/jpr.2016.5.

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Abstract De Bruijn's identity relates two important concepts in information theory: Fisher information and differential entropy. Unlike the common practice in the literature, in this paper we consider general additive non-Gaussian noise channels where more realistically, the input signal and additive noise are not independently distributed. It is shown that, for general dependent signal and noise, the first derivative of the differential entropy is directly related to the conditional mean estimate of the input. Then, by using Gaussian and Farlie–Gumbel–Morgenstern copulas, special versions of the result are given in the respective case of additive normally distributed noise. The previous result on independent Gaussian noise channels is included as a special case. Illustrative examples are also provided.

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17

ISAKA, Motohiko. "Oblivious Transfer from the Additive White Gaussian Noise Channel." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E93-A, no. 2 (2010): 516–25. http://dx.doi.org/10.1587/transfun.e93.a.516.

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18

Artail, Hassan A., and Jatinder S. Bedi. "A new receiver for additive white Gaussian noise channels." Integrated Computer-Aided Engineering 7, no. 2 (April 1, 2000): 169–80. http://dx.doi.org/10.3233/ica-2000-7206.

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19

Prasetyo, Heri, and Umi Salamah. "Swarm Intelligence for Additive White Gaussian Noise Level Estimation." INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 20, no. 3 (September 30, 2020): 169–80. http://dx.doi.org/10.5391/ijfis.2020.20.3.169.

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20

Lapidoth, A. "Nearest neighbor decoding for additive non-Gaussian noise channels." IEEE Transactions on Information Theory 42, no. 5 (1996): 1520–29. http://dx.doi.org/10.1109/18.532892.

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21

Mora, M. D., A. Germani, and A. Nardecchia. "Restoration of images corrupted by additive non-Gaussian noise." IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 48, no. 7 (July 2001): 859–75. http://dx.doi.org/10.1109/81.933327.

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22

Lim, Teck Por, and Sadasivan Puthusserypady. "Chaotic time series prediction and additive white Gaussian noise." Physics Letters A 365, no. 4 (June 2007): 309–14. http://dx.doi.org/10.1016/j.physleta.2007.01.027.

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23

Li, Hui, Stefan M. Moser, and Dongning Guo. "Capacity of the Memoryless Additive Inverse Gaussian Noise Channel." IEEE Journal on Selected Areas in Communications 32, no. 12 (December 2014): 2315–29. http://dx.doi.org/10.1109/jsac.2014.2367673.

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24

Saha, Surajit, Suvajit Pal, Jayanta Ganguly, and Manas Ghosh. "Exploring Optical Dielectric Function of Impurity Doped Quantum Dots in Presence of Gaussian White Noise." Journal of Advanced Physics 6, no. 1 (March 1, 2017): 48–55. http://dx.doi.org/10.1166/jap.2017.1294.

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We investigate the total optical dielectric function (TODF) of impurity doped quantum dot (QD) in presence and absence of noise. Noise invoked is Gaussian white noise and the QD is doped with Gaussian impurity. Noise has been introduced to the system additively and multiplicatively. The TODF profiles have been monitored as a function of incident photon energy for different values of several important parameters. Moreover, the role of mode of application of noise (additive/multiplicative) on the TODF profiles has also been meticulously analyzed. We have found that the shift of TODF peak position and change in TODF peak height sensitively depend on presence/absence of noise and also on its mode of application. Introduction of multiplicative noise causes greater deviation of TODF profiles from that of noise-free condition than using additive noise.

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25

Alabbasi, Hesham A., Ali M. Jalil, and Fadhil S. Hasan. "Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5208. http://dx.doi.org/10.11591/ijece.v10i5.pp5208-5216.

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The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios.

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26

Dytso, Alex, Martina Cardone, and H. Vincent Poor. "On Estimating the Norm of a Gaussian Vector Under Additive White Gaussian Noise." IEEE Signal Processing Letters 26, no. 9 (September 2019): 1325–29. http://dx.doi.org/10.1109/lsp.2019.2929863.

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27

Kirmizitas, Hikmet, and Nurettin Besli. "Image and Texture Independent Deep Learning Noise Estimation Using Multiple Frames." Elektronika ir Elektrotechnika 28, no. 6 (December 21, 2022): 42–47. http://dx.doi.org/10.5755/j02.eie.30586.

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In this study, a novel multiple frame based image and texture independent Convolutional Neural Network (CNN) noise estimator is introduced. Noise estimation is a crucial step for denoising algorithms, especially for ones that are called “non-blind”. The estimator works for additive Gaussian noise for varying noise levels. The noise levels studied in this work have a standard deviation equal to 5 to 25 increasing 5 by 5. Since there is no database for noisy multiple images to train and validate the network, two frames of synthetic noisy images with a variety of noise levels are created by adding Additive White Gaussian Noise (AWGN) to each clean image. The proposed method is applied on the most popular gray level images besides the color image databases such as Kodak, McMaster, BSDS500 in order to compare the results with the other works. Image databases comprise indoor and outdoor scenes that have fine details and richer texture. The estimator has an accuracy rate of 99 % for the classification and favourable results for the regression. The proposed method outperforms traditional methods in most cases. And the regression output can be used with any non-blind denoising method.

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28

Guo, Yong-Feng, Bei Xi, Fang Wei, and Jian-Guo Tan. "Stochastic resonance in FitzHugh–Nagumo neural system driven by correlated non-Gaussian noise and Gaussian noise." International Journal of Modern Physics B 31, no. 32 (December 18, 2017): 1750264. http://dx.doi.org/10.1142/s0217979217502642.

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In this paper, the phenomenon of stochastic resonance in FitzHugh–Nagumo (FHN) neural system driven by correlated non-Gaussian noise and Gaussian white noise is investigated. First, the analytical expression of the stationary probability distribution is derived by using the path integral approach and the unified colored noise approximation. Then, we obtain the expression of signal-to-noise ratio (SNR) by applying the theory of two-state model. The results show that the phenomena of stochastic resonance and multiple stochastic resonance appear in FHN neural system under different values of parameters. The effects of the multiplicative noise intensity D and the additive noise intensity Q on the SNR are entirely different. In addition, the discharge behavior of FHN neural system is restrained when the value of Q is smaller. But, it is conducive to enhance signal response of FHN neural system when the values of Q and D are relatively larger.

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29

Guo, Yong-Feng, Bei Xi, Fang Wei, and Jian-Guo Tan. "The mean first-passage time in simplified FitzHugh–Nagumo neural model driven by correlated non-Gaussian noise and Gaussian noise." Modern Physics Letters B 32, no. 28 (October 4, 2018): 1850339. http://dx.doi.org/10.1142/s0217984918503396.

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In this paper, the mean first-passage time (MFPT) in simplified FitzHugh–Nagumo (FHN) neural model driven by correlated multiplicative non-Gaussian noise and additive Gaussian white noise is studied. Firstly, using the path integral approach and the unified colored-noise approximation (UCNA), the analytical expression of the stationary probability distribution (SPD) is derived, and the validity of the approximation method employed in the derivation is checked by performing numerical simulation. Secondly, the expression of the MFPT of the system is obtained by applying the definition and the steepest-descent method. Finally, the effects of the multiplicative noise intensity D, the additive noise intensity Q, the noise correlation time [Formula: see text], the cross-correlation strength [Formula: see text] and the non-Gaussian noise deviation parameter q on the MFPT are discussed.

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30

Mutothya, Nicholas Mwilu, Yong Xu, Yongge Li, Ralf Metzler, and Nicholas Muthama Mutua. "First passage dynamics of stochastic motion in heterogeneous media driven by correlated white Gaussian and coloured non-Gaussian noises." Journal of Physics: Complexity 2, no. 4 (November 23, 2021): 045012. http://dx.doi.org/10.1088/2632-072x/ac35b5.

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Abstract We study the first passage dynamics for a diffusing particle experiencing a spatially varying diffusion coefficient while driven by correlated additive Gaussian white noise and multiplicative coloured non-Gaussian noise. We consider three functional forms for position dependence of the diffusion coefficient: power-law, exponential, and logarithmic. The coloured non-Gaussian noise is distributed according to Tsallis’ q-distribution. Tracks of the non-Markovian systems are numerically simulated by using the fourth-order Runge–Kutta algorithm and the first passage times (FPTs) are recorded. The FPT density is determined along with the mean FPT (MFPT). Effects of the noise intensity and self-correlation of the multiplicative noise, the intensity of the additive noise, the cross-correlation strength, and the non-extensivity parameter on the MFPT are discussed.

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31

Wang, Kang-Kang, Ya-Jun Wang, Hui Ye, and Sheng-Hong Li. "Time delay and cross-correlated Gaussian noises-induced stochastic stability and regime shift between steady states for an insect outbreak system." International Journal of Biomathematics 12, no. 04 (May 2019): 1950048. http://dx.doi.org/10.1142/s1793524519500487.

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In this paper, we focus on investigating the stochastic stability and the regime transition between the endangered state and the boom state for a time-delayed insect growth system driven by correlated external and internal noises. By use of the Fokker–Planck equation, the method of small time delay approximation and the fast descent method, we explore in detail the joint action of noise terms and time delay on the mean reproduction and depression time for the insect population. Our investigations indicate that the pseudo-resonance phenomenon of the mean first-passage time (MFPT) occurs because of the impact of different noises and time delay. Through the numerical calculation, it is discovered that multiplicative noise can speed up the shift of the insect population from the boom state to the endangered one, while the noise correlation and time delay can propel the insect system to evolve from the endangered state to the boom state and improve the biological stability. In addition, the impact of the additive noise on the stability of the biological system depends on the positive and negative situation of the noise correlation. On the other hand, during the process of suppressing the insect explosion, it is beneficial to the pest control to amplify the association noise strength and weaken the intensities of the multiplicative, additive noises and time delay. However, during the process of eliminating the pests, it can produce nice effect on the disinsection to increase time delay, the intensities of multiplicative and additive noises and weaken the strength of noise correlation.

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32

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 (October 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 the acoustic noise is colored noise instead of white noise. Intermittent noise in the oceans and seas frequently includes significant Non-Gaussian elements. The samples noise data are analyzed in actual time for various depths of 1 meter, 3 meter and 5 meter in order to limit the statistical properties of underwater acoustic noise in Lake Hamrin, Diyala, Iraq such as the autocorrelation function (ACF), the probability density function (PDF) and also power spectral density (PSD). The experimental results showed that the noise of the Tigris river is a color noise and does not follow the Gaussian distribution.</p>

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33

Kittisuwan, Pichid. "Medical image denoising using simple form of MMSE estimation in Poisson–Gaussian noise model." International Journal of Biomathematics 09, no. 02 (January 14, 2016): 1650020. http://dx.doi.org/10.1142/s1793524516500200.

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Poisson–Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson–Gaussian noise reduction algorithm has shown remarkable success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson–Gaussian noise into an additive white Gaussian noise (AWGN). So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error (MMSE) estimator for logistic distribution in Poisson–Gaussian noise. The experimental results show that the proposed method yields good denoising results.

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34

Trabelsi, Abdelaziz, Otmane Ait Mohamed, and Yves Audet. "Robust Parametric Modeling of Speech in Additive White Gaussian Noise." Journal of Signal and Information Processing 06, no. 02 (2015): 99–108. http://dx.doi.org/10.4236/jsip.2015.62010.

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35

Artyushenko, V. M., and V. I. Volovach. "Measuring information signal parameters under additive non-Gaussian correlated noise." Optoelectronics, Instrumentation and Data Processing 52, no. 6 (November 2016): 546–51. http://dx.doi.org/10.3103/s8756699016060030.

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36

FUJITA, Hachiro. "Secrecy Capacity of Wiretap Channels with Additive Colored Gaussian Noise." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A, no. 6 (2015): 1276–87. http://dx.doi.org/10.1587/transfun.e98.a.1276.

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37

Le, Duc-Anh, Hung V. Vu, Nghi H. Tran, Mustafa Cenk Gursoy, and Tho Le-Ngoc. "Approximation of Achievable Rates in Additive Gaussian Mixture Noise Channels." IEEE Transactions on Communications 64, no. 12 (December 2016): 5011–24. http://dx.doi.org/10.1109/tcomm.2016.2602342.

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38

Guotong Zhou and G. B. Giannakis. "Harmonics in Gaussian multiplicative and additive noise: Cramer-Rao bounds." IEEE Transactions on Signal Processing 43, no. 5 (May 1995): 1217–31. http://dx.doi.org/10.1109/78.382405.

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39

Mukherjee, Arindum, Shantanu Mandal, Dia Ghosh, and B. N. Biswas. "Influence of Additive White Gaussian Noise on the OEO Output." IEEE Journal of Quantum Electronics 57, no. 1 (February 2021): 1–10. http://dx.doi.org/10.1109/jqe.2020.3038464.

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40

Sheikh-Hosseini, Mohsen, and Ghosheh Abed Hodtani. "On the capacity of additive white mixture Gaussian noise channels." Transactions on Emerging Telecommunications Technologies 30, no. 7 (February 13, 2019): e3585. http://dx.doi.org/10.1002/ett.3585.

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NUALART, DAVID, and LLUÍS QUER-SARDANYONS. "OPTIMAL GAUSSIAN DENSITY ESTIMATES FOR A CLASS OF STOCHASTIC EQUATIONS WITH ADDITIVE NOISE." Infinite Dimensional Analysis, Quantum Probability and Related Topics 14, no. 01 (March 2011): 25–34. http://dx.doi.org/10.1142/s0219025711004286.

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In this note, we establish optimal lower and upper Gaussian bounds for the density of the solution to a class of stochastic integral equations driven by an additive spatially homogeneous Gaussian random field. The proof is based on the techniques of the Malliavin calculus and a density formula obtained by Nourdin and Viens. Then, the main result is applied to the mild solution of a general class of SPDEs driven by a Gaussian noise which is white in time and has a spatially homogeneous correlation. In particular, this covers the case of the stochastic heat and wave equations in ℝd with d ≥ 1 and d ∈ {1, 2, 3}, respectively. The upper and lower Gaussian bounds have the same form and are given in terms of the variance of the stochastic integral term in the mild form of the equation.

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Cui, Ge. "Application of Addition and Multiplication Noise Model Parameter Estimation in INSAR Image Processing." Mathematical Problems in Engineering 2022 (May 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/3164513.

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INSAR images are inevitably contaminated by noise during the process of generation, transmission, compression, and reception. Noise not only affects the quality of the INSAR image, but also affects subsequent operations such as the design of corresponding filters, INSAR image segmentation, compression, restoration, and feature recognition. The INSAR image noise model is mainly divided into additive noise and multiplicative noise. Compared with additive noise, multiplicative noise is more complicated due to INSAR image correlation and non-Gaussian. Based on least squares algorithm system of additive and multiplicative mixed noise model, this paper proposes a method of using PCA to remove multiplicative gamma distribution noise. The pure noise coefficient is obtained by subtracting the original coefficient from the diagonal wavelet coefficient of the noisy image, and the mode of the local variance is calculated as the estimation value of the noise standard deviation. Experiments show that the proposed method can obtain more accurate estimation of noise; in particular in the case of less noise and more detailed image information, its effect is more obvious.

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Kittisuwan, Pichid. "Image enhancement via MMSE estimation of Gaussian scale mixture with Maxwell density in AWGN." Journal of Innovative Optical Health Sciences 09, no. 02 (March 2016): 1650021. http://dx.doi.org/10.1142/s1793545816500218.

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In optical techniques, noise signal is a classical problem in medical image processing. Recently, there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data. In wavelet domain, if Bayesian estimator is used for denoising problem, the solution requires a prior knowledge about the distribution of wavelet coefficients. Indeed, wavelet coefficients might be better modeled by super Gaussian density. The super Gaussian density can be generated by Gaussian scale mixture (GSM). So, we present new minimum mean square error (MMSE) estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise (AWGN). We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.

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Guo, Yongfeng, Xiaojuan Lou, Qiang Dong, and Linjie Wang. "Stochastic resonance in a periodic potential system driven by cross-correlated noises and periodic signal." International Journal of Modern Physics B 33, no. 28 (November 10, 2019): 1950338. http://dx.doi.org/10.1142/s0217979219503387.

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In this paper, the stochastic resonance (SR) in a periodic potential system driven by cross-correlated noises and periodic signal is investigated. The signal-to-noise ratio (SNR) is used to characterize the SR. Using the algorithm of fourth-order Runge–Kutta, we obtain the curves of SNR for different parameters. The effects of some system parameters, additive Gaussian white noise and multiplicative Gaussian colored noise intensity on SR are characterized by analyzing SNR curves. When increasing system parameter and noise cross-correlation strength in SNR-D, the SR of the system can be enhanced. However, the SR will be weakened by increasing other parameters. Otherwise, the phenomena in SNR-Q are opposite to in SNR-D when increasing signal amplitude and correlation time.

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Tang, Song Yuan. "A Non-Local Image Denoising Technique Using Adaptive Filter Parameter." Applied Mechanics and Materials 556-562 (May 2014): 4839–42. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4839.

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This paper proposes a method to obtain the optimal filter parameter of the non-local mean (NLM) algorithm. The parameter is assumed to be a function of the variance of the additive white Gaussian noise and is adaptive estimated. The initialization of the variance of the additive white Gaussian noise is estimated by Wiener filter. Then the NLM filter is used to adaptively estimate the noise variance. The image denoising is an iterative computation till the parameter convergence. Experiments show that the proposed method can improve the quality of the denoised images efficiently.

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Liu, Qiang. "Rate-compatible LDPC convolutional codes over non-gaussian noise channel." MATEC Web of Conferences 309 (2020): 01010. http://dx.doi.org/10.1051/matecconf/202030901010.

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This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of rate-compatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.

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Reddy, B. Lokesh, and Anith Nelleri. "Convex optimization for additive noise reduction in quantitative complex object wave retrieval using compressive off-axis digital holographic imaging." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 706–15. http://dx.doi.org/10.1515/jisys-2022-0043.

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Abstract Image denoising is one of the important problems in the research field of computer vision, artificial intelligence, 3D vision, and image processing, where the fundamental aim is to recover the original image features from a noisy contaminated image. The camera sensor additive noise present in the holographic recording process reduces the quality of the retrieved image. Even though various techniques have been developed to minimize the noise in digital holography, the noise reduction still remains a challenging task. This article presents a compressive sensing (CS) technique to minimize the additive noise in the digital holographic reconstruction process. We demonstrate the reduction of additive noise using complex wave retrieval method as a sensing matrix in the CS model. The proposed CS method to suppress the noise during the reconstruction process is illustrated using numerical simulations. Only 50% of the pixel measurements are considered in the noisy hologram, which is far less than the original complex object pixels. The impact of additive gaussian noise in the recording plane on the reconstruction accuracy of both intensity and phase distribution is analysed. The CS method denoises and estimates the complex object information accurately. The numerical simulation results have shown that the proposed CS method has effectively minimized the noise in the reconstructed image and has greatly improved the quality of both intensity and phase information.

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Brekhna, Brekhna, Arif Mahmood, and Yuanfeng Zhou. "Robustness analysis of superpixel algorithms to image blur, additive Gaussian noise, and impulse noise." Journal of Electronic Imaging 26, no. 06 (August 24, 2017): 1. http://dx.doi.org/10.1117/1.jei.26.6.061604.

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Sahu, Sima, Harsh Vikram Singh, Basant Kumar, and Amit Kumar Singh. "A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images." Journal of Intelligent Systems 29, no. 1 (January 10, 2018): 189–201. http://dx.doi.org/10.1515/jisys-2017-0402.

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Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

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Jondral, Friedrich K. "White Gaussian Noise – Models for Engineers." Frequenz 72, no. 5-6 (April 25, 2018): 293–99. http://dx.doi.org/10.1515/freq-2017-0064.

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AbstractThis paper assembles some information about white Gaussian noise (WGN) and its applications. It starts from a description of thermal noise, i. e. the irregular motion of free charge carriers in electronic devices. In a second step, mathematical models of WGN processes and their most important parameters, especially autocorrelation functions and power spectrum densities, are introduced. In order to proceed from mathematical models to simulations, we discuss the generation of normally distributed random numbers. The signal-to-noise ratio as the most important quality measure used in communications, control or measurement technology is accurately introduced. As a practical application of WGN, the transmission of quadrature amplitude modulated (QAM) signals over additive WGN channels together with the optimum maximum likelihood (ML) detector is considered in a demonstrative and intuitive way.

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