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Artykuły w czasopismach na temat „Additive Gaussian noise”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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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 (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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2

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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3

Idel, Martin, and Robert Konig. "On quantum additive Gaussian noise channels." Quantum Information and Computation 17, no. 3&4 (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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4

CHAPEAU-BLONDEAU, FRANÇOIS, and DAVID ROUSSEAU. "CONSTRUCTIVE ACTION OF ADDITIVE NOISE IN OPTIMAL DETECTION." International Journal of Bifurcation and Chaos 15, no. 09 (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

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5

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

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6

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 (2010): 2471–78. http://dx.doi.org/10.1049/iet-cta.2009.0449.

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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.

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8

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 (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 wea

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9

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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10

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 (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 wi

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11

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 (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

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12

Ruggeri, G., and S. Mancin. "Quantum Gaussian channels with additive correlated classical noise." Quantum Information and Computation 7, no. 3 (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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13

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

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14

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

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15

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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16

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 (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 positio

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17

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 (2017): 1–9. http://dx.doi.org/10.1016/j.cjph.2016.11.004.

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18

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 (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

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19

Hesham, A. Alabbasi, M. Jalil Ali, and S. Hasan Fadhil. "Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5208–16. https://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

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20

Kirmizitas, Hikmet, and Nurettin Besli. "Image and Texture Independent Deep Learning Noise Estimation Using Multiple Frames." Elektronika ir Elektrotechnika 28, no. 6 (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 addin

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21

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 (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 st

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22

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 (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 record

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23

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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24

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

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25

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 (2020): 169–80. http://dx.doi.org/10.5391/ijfis.2020.20.3.169.

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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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27

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 (2001): 859–75. http://dx.doi.org/10.1109/81.933327.

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

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29

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 (2014): 2315–29. http://dx.doi.org/10.1109/jsac.2014.2367673.

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30

Kittisuwan, Pichid. "Medical image denoising using simple form of MMSE estimation in Poisson–Gaussian noise model." International Journal of Biomathematics 09, no. 02 (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 p

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31

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 (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 para

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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 (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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33

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

Asem, Khmag, Ghoul Sami, Abdul Rahman Al-Haddad Syed, and Kamarudin Noraziahtulhidayu. "Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 2 (2018): 915–24. https://doi.org/10.12928/TELKOMNIKA.v16i2.9060.

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In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested im

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35

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 (2019): 1325–29. http://dx.doi.org/10.1109/lsp.2019.2929863.

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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 (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

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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 add

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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 (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

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39

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 (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 squ

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40

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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41

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 si

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Ergu, Yared Abera. "Medical Image Mixed Denoise using Discrete Multi Wavlet Transform Novel Threshold Method." International Journal of Technology Information and Computer (TIJOTIC) 1, no. 1 (2020): 16–28. https://doi.org/10.5281/zenodo.3888529.

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Generally, most of the<strong> </strong>images are corrupted by noise which is solved by denoising techniques in the image processing.  For that single thresholding techniques are used which removes the additive random noise. The Gaussian -Multi Wavelet technique is utilized to denoising the Gaussian noise present in the mammogram image which is an efficient method due to the capability to acquire the signal energies in few transforms value. In order to enhance and the noise present in the digital mammographics image, the novel Multi Wavelet techniques are used in this paper.  In the

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Varga, Domonkos. "Understanding How Image Quality Affects Transformer Neural Networks." Signals 5, no. 3 (2024): 562–79. http://dx.doi.org/10.3390/signals5030031.

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Deep learning models, particularly transformer architectures, have revolutionized various computer vision tasks, including image classification. However, their performance under different types and levels of noise remains a crucial area of investigation. In this study, we explore the noise sensitivity of prominent transformer models trained on the ImageNet dataset. We systematically evaluate 22 transformer variants, ranging from state-of-the-art large-scale models to compact versions tailored for mobile applications, under five common types of image distortions. Our findings reveal diverse sen

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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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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 (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 an

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Liu, Qi, Yongchao Yu, Boon Siew Han, and Wei Zhou. "An Improved Spectral Subtraction Method for Eliminating Additive Noise in Condition Monitoring System Using Fiber Bragg Grating Sensors." Sensors 24, no. 2 (2024): 443. http://dx.doi.org/10.3390/s24020443.

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The additive noise in the condition monitoring system using fiber Bragg grating (FBG) sensors, including white Gaussian noise and multifrequency interference, has a significantly negative influence on the fault diagnosis of rotating machinery. Spectral subtraction (SS) is an effective method for handling white Gaussian noise. However, the SS method exhibits poor performance in eliminating multifrequency interference because estimating the noise spectrum accurately is difficult, and it significantly weakens the useful information components in measured signals. In this study, an improved spectr

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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 (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 minimiz

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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 (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, si

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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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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 (2016): 546–51. http://dx.doi.org/10.3103/s8756699016060030.

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