Relevant bibliographies by topics / Additive White Gaussian Noise (AWGN)

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Academic literature on the topic 'Additive White Gaussian Noise (AWGN)'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 June 2025

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Journal articles on the topic "Additive White Gaussian Noise (AWGN)"

1

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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2

Huang, Chengwei, Guoming Chen, Hua Yu, Yongqiang Bao, and Li Zhao. "Speech Emotion Recognition under White Noise." Archives of Acoustics 38, no. 4 (2013): 457–63. http://dx.doi.org/10.2478/aoa-2013-0054.

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Abstract Speaker‘s emotional states are recognized from speech signal with Additive white Gaussian noise (AWGN). The influence of white noise on a typical emotion recogniztion system is studied. The emotion classifier is implemented with Gaussian mixture model (GMM). A Chinese speech emotion database is used for training and testing, which includes nine emotion classes (e.g. happiness, sadness, anger, surprise, fear, anxiety, hesitation, confidence and neutral state). Two speech enhancement algorithms are introduced for improved emotion classification. In the experiments, the Gaussian mixture model is trained on the clean speech data, while tested under AWGN with various signal to noise ratios (SNRs). The emotion class model and the dimension space model are both adopted for the evaluation of the emotion recognition system. Regarding the emotion class model, the nine emotion classes are classified. Considering the dimension space model, the arousal dimension and the valence dimension are classified into positive regions or negative regions. The experimental results show that the speech enhancement algorithms constantly improve the performance of our emotion recognition system under various SNRs, and the positive emotions are more likely to be miss-classified as negative emotions under white noise environment.
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3

Liu, Haoqiang, Hongbo Zhao, Xiaowen Chen, and Wenquan Feng. "An optimized initialization for LDPC decoding over GF(q) in impulsive noise environments." PLOS ONE 16, no. 5 (2021): e0250930. http://dx.doi.org/10.1371/journal.pone.0250930.

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Modern navigation satellite communication has the characteristic of high transmitting rate. To avoid bit errors in data transmission, low density parity check (LDPC) codes are widely recognized as efficient ways for navigation communication. Conventionally, the LDPC decoding is applied for additive white Gaussian noise (AWGN) channel and degrades severely while facing the impulsive noise. However, navigation communication often suffers from impulsive interference due to the occurrence of high amplitude “spikes”. At this time, the conventional Gaussian noise assumption is inadequate. The impulsive component of interference has been found to be significant which influences the reliability of transmitted information. Therefore the LDPC decoding algorithms for AWGN channel are not suitable for impulsive noise environments. Consider that LDPC codes over GF(q) perform better than binary LDPC in resisting burst errors for current navigation system, it is necessary to conduct research on LDPC codes over GF(q). In this paper, an optimized initialization by calculating posterior probabilities of received symbols is proposed for non-binary LDPC decoding on additive white Class A noise (AWAN) channel. To verify the performance of the proposed initialization, extensive experiments are performed in terms of convergence, validity, and robustness. Preliminary results demonstrate that the decoding algorithm with the optimized initialization for non-binary LDPC codes performs better than the competing methods and that of binary LDPC codes on AWAN channel.
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4

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 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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5

Arora, Sahil, and Nirvair Neeru. "Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter." International Journal of Computer Applications 146, no. 9 (2016): 17–24. http://dx.doi.org/10.5120/ijca2016910854.

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6

Zhou, Yuqian, Jianbo Jiao, Haibin Huang, et al. "When AWGN-Based Denoiser Meets Real Noises." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 13074–81. http://dx.doi.org/10.1609/aaai.v34i07.7009.

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Discriminative learning based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-the-art performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at https://github.com/yzhouas/PD-Denoising-pytorch.
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7

Thakur, Mohit, and Gerhard Kramer. "Quasi-Concavity for Gaussian Multicast Relay Channels." Entropy 21, no. 2 (2019): 109. http://dx.doi.org/10.3390/e21020109.

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Standard upper and lower bounds on the capacity of relay channels are cut-set (CS), decode-forward (DF), and quantize-forward (QF) rates. For real additive white Gaussian noise (AWGN) multicast relay channels with one source node and one relay node, these bounds are shown to be quasi-concave in the receiver signal-to-noise ratios and the squared source-relay correlation coefficient. Furthermore, the CS rates are shown to be quasi-concave in the relay position for a fixed correlation coefficient, and the DF rates are shown to be quasi-concave in the relay position. The latter property characterizes the optimal relay position when using DF. The results extend to complex AWGN channels with random phase variations.
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8

Li, Chang, Yu Liu, Juan Cheng, et al. "Hyperspectral Unmixing with Bandwise Generalized Bilinear Model." Remote Sensing 10, no. 10 (2018): 1600. http://dx.doi.org/10.3390/rs10101600.

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Generalized bilinear model (GBM) has received extensive attention in the field of hyperspectral nonlinear unmixing. Traditional GBM unmixing methods are usually assumed to be degraded only by additive white Gaussian noise (AWGN), and the intensity of AWGN in each band of hyperspectral image (HSI) is assumed to be the same. However, the real HSIs are usually degraded by mixture of various kinds of noise, which include Gaussian noise, impulse noise, dead pixels or lines, stripes, and so on. Besides, the intensity of AWGN is usually different for each band of HSI. To address the above mentioned issues, we propose a novel nonlinear unmixing method based on the bandwise generalized bilinear model (NU-BGBM), which can be adapted to the presence of complex mixed noise in real HSI. Besides, the alternative direction method of multipliers (ADMM) is adopted to solve the proposed NU-BGBM. Finally, extensive experiments are conducted to demonstrate the effectiveness of the proposed NU-BGBM compared with some other state-of-the-art unmixing methods.
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9

Ni'amah, Khoirun, Muhammad Panji Kusuma Praja, and Yuninda Dwianti Marimbun. "Comparative Analysis of 16-QAM and 64-QAM Modulation in Additive White Gaussian Noise and Rayleigh Fading Channels." CESS (Journal of Computer Engineering, System and Science) 7, no. 1 (2021): 90. http://dx.doi.org/10.24114/cess.v7i1.26729.

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This reseach simulates and analyzes paramaters bit error rate (BER) of 16-QAM and 64-QAM modulation on Additive White Gaussian Noise and Rayleigh Fading channels. This research aims to determine 5G modulation with the level of data quality after the transmission process is carried out. The modulation simulation results obtained will be compared with the theoretical bit error rate (BER). The simulation results obtained from the two channel scenarios used are 16-QAM modulation reaching BER 10-4, AWGN channel only requires 15 dB Eb/N0 and for Rayleigh Fading channel it requires 38 dB Eb/N0. The BER theoretical results obtained for the 16-QAM modulation of the AWGN channel have a difference of 3 dB with the simulation results, while for the Rayleigh Fading channel it is 5 dB. Then, the simulation results of 64-QAM modulation AWGN channel to achieve BER 10-4 requires Eb/N0 of 24.6 dB, Rayleigh Fading of 47 dB. The theoretical results of BER obtained for the 64-QAM modulation of the AWGN channel have a difference of 1 dB with the simulation results, while for the Rayleigh Fading channel it is 0.5 dB. In this study, between 16-QAM and 64-QAM 5G modulation is more suitable to use 16-QAM modulation because it requires less power to achieve the desired BER 10-4.
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10

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 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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