Theoretical investigation of Gaussian and non-Gaussian noise masking properties

Sokolov, Rostislav I.

doi:10.1109/icieam.2016.7911554

Cited by 5 publications

(3 citation statements)

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“…Several types of distortion might exist. Some of the common types that often exist are Gaussian, Impulse and Speckle Noise, where Gaussian Noise is additive, speckle is multiplicative and Impulse Noise is a combination of both [2] [3]. At the same time we observe that different kind of noise might exist in a single image, with varying intensities.…”

Section: Introductionmentioning

confidence: 80%

Efficient Removal of Noise from Image using Non-Linear Filtering

Ghosh¹,

Roy²,

Das³

2019

International Journal of Innovative Research in Electrical, Ele

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An image may contain different types of information related to parameters such as colour and brightness, the irregular variation of which leads to noise in an image. Images often get corrupted by different kind of noise such as Impulse, Gaussian and Salt-and-Pepper Noise, during image acquisition or transmission caused by the digital sensor attempting to record/capture tiny amount of light. Since an image appears in multiple resolutions in different forms, an image can be segmented into different classifying features. As a result, signal and noise can be separated and elimination of noise becomes easier. In this article, a non-linear median filter is preferred, which is found to be useful in reducing not only Impulse Noise but also Gaussian and Speckle Noise. The corresponding analyses are performed on grayscale images, where the results obtained through non-linear filtering are superior to results obtained using Average, Gaussian and Weiner filters.

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Section: Introductionmentioning

confidence: 80%

Efficient Removal of Noise from Image using Non-Linear Filtering

Ghosh¹,

Roy²,

Das³

2019

International Journal of Innovative Research in Electrical, Ele

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“…The images' saturation, brightness, contrast, and sharpness are randomly adjusted; and 4. Gaussian noise is added to images [23].…”

Section: Data Acquisition and Constructionmentioning

confidence: 99%

Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks

et al. 2020

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Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.

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“…However, in the VLF channel, the noise distribution has the characteristics of a heavy tail and is significantly non-Gaussian. [11][12][13][14][15] Such non-Gaussian noise consideration is due to the influence of atmospheric thunder and lightning. To achieve optimal reception in submarine multi-antenna communications, it is necessary to study multi-antenna receiving technology in a non-Gaussian noise environment.…”

Section: Introductionmentioning

confidence: 99%

Blind receiver design-based multi-dimensional stable distribution model using the EM algorithm

Ying,

Xiao,

et al. 2023

Advances in Mechanical Engineering

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Multi-antenna receivers are a key technology for modern communication systems. Signal attenuation in the very low frequency (VLF) channel is a serious problem. In addition to high signal attenuation, the resulting noise on the VLF channel is non-Gaussian. Hence, to analyze and address the issue of the normal operation of the multi-antenna receiver in the VLF channel, we must study the signal detection problem in a multi-dimensional non-Gaussian fading channel. Motivated by the existing blind receiver in an underwater submarine single-antenna receiver, we study the signal detection and estimation algorithm under the fading channel for a multi-dimensional non-Gaussian noise model. In this study, we propose a blind receiver based on the expectation-maximization (EM) algorithm. The proposed blind receiver can reduce non-Gaussian noise. In addition, we propose a nonlinear receiver that can accurately receive the transmitted signal over the high-attenuation VLF communication systems. Numerical and simulation results over uncorrelated and correlated non-Gaussian channels confirm that the design of the proposed blind receiver is close to optimal, with low computation complexity.

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Theoretical investigation of Gaussian and non-Gaussian noise masking properties

Cited by 5 publications

References 0 publications

Efficient Removal of Noise from Image using Non-Linear Filtering

Efficient Removal of Noise from Image using Non-Linear Filtering

Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks

Blind receiver design-based multi-dimensional stable distribution model using the EM algorithm

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