scholarly journals An Improved Structure-Based Gaussian Noise Variance Estimation Method for Noisy Images

2013 ◽  
Vol 17 (6) ◽  
pp. 299-305 ◽  
Author(s):  
Chong Yi ◽  
Tetsuya Shimamura
Keyword(s):  
Gaussian Noise ◽  
Variance Estimation ◽  
Estimation Method ◽  
Noise Variance ◽  
Noisy Images

scholarly journals Data Denoising In Analog And Digital Domains

2021 ◽  
Author(s):  
Amroabadi S. Hashemi
Keyword(s):  
Mean Square Error ◽  
Variance Estimation ◽  
Hyperspectral Image ◽  
Dead Zone ◽  
Estimation Method ◽  
Noise Removal ◽  
Mean Square ◽  
Noise Variance ◽  
Worst Case ◽  
Soft Thresholding

In this thesis, we develop various methods for the purpose of data denoising. We propose a method for Mean Square Error (MSE) estimation in Soft Thresholding. The MSE estimator is based on Minimum Noiseless Data Length (MNDL). Our simulation results show that this MSE estimate is a valuable comparison measure for different soft thresholding methods. Two denoising methods are proposed for analog domain: Mean Square Error EstiMation (MSEEM) which minimizes the worst case MSE estimate, and Noise Invalidation Denoising (NIDe) method which is based on the newly prosposed idea of noise signature. While MSEEM shown to be the optimum denoising method for non-sparse signals, NIDe approach outperforms the other well known denoising methods in presence of colored noise. In digital domain we address two interesting problems: 1) simultaneous denoising and quantization method, 2) denoising a digital signal in digital domain. For problem one, we propose a new method that generalizes the idea of dead zone estimation to a multi-level noise removal. An example of this method is shown for hyperspectral image denoising and compression. A digital domain denoising approach pioneers in answering the second problem with only one prior knowledge on the desired signal, that it is digital. The method provides the optimum reconstruction levels in the MSE sense. One of the critical steps of denoising process is the noise variance estimation. As a part of this thesis, we propose a novel noise variance estimation method for BayesShrink that outperforms conventional MAD-based noise variance estimation. Although BayesShrink is one of the most efficient denoising methods, no analytical analysis is available for it. Here, we study Bayes estimators for General Gaussian Distribued (GGD) data and provide the theoretical justification for BayesShrink. This study enables us to generalize the BayesShrink threshold to Generalized BayesShrink which outperforms the BayesShrink itself.

scholarly journals Data Denoising In Analog And Digital Domains

2021 ◽  
Author(s):  
Amroabadi S. Hashemi
Keyword(s):  
Mean Square Error ◽  
Variance Estimation ◽  
Hyperspectral Image ◽  
Dead Zone ◽  
Estimation Method ◽  
Noise Removal ◽  
Mean Square ◽  
Noise Variance ◽  
Worst Case ◽  
Soft Thresholding

In this thesis, we develop various methods for the purpose of data denoising. We propose a method for Mean Square Error (MSE) estimation in Soft Thresholding. The MSE estimator is based on Minimum Noiseless Data Length (MNDL). Our simulation results show that this MSE estimate is a valuable comparison measure for different soft thresholding methods. Two denoising methods are proposed for analog domain: Mean Square Error EstiMation (MSEEM) which minimizes the worst case MSE estimate, and Noise Invalidation Denoising (NIDe) method which is based on the newly prosposed idea of noise signature. While MSEEM shown to be the optimum denoising method for non-sparse signals, NIDe approach outperforms the other well known denoising methods in presence of colored noise. In digital domain we address two interesting problems: 1) simultaneous denoising and quantization method, 2) denoising a digital signal in digital domain. For problem one, we propose a new method that generalizes the idea of dead zone estimation to a multi-level noise removal. An example of this method is shown for hyperspectral image denoising and compression. A digital domain denoising approach pioneers in answering the second problem with only one prior knowledge on the desired signal, that it is digital. The method provides the optimum reconstruction levels in the MSE sense. One of the critical steps of denoising process is the noise variance estimation. As a part of this thesis, we propose a novel noise variance estimation method for BayesShrink that outperforms conventional MAD-based noise variance estimation. Although BayesShrink is one of the most efficient denoising methods, no analytical analysis is available for it. Here, we study Bayes estimators for General Gaussian Distribued (GGD) data and provide the theoretical justification for BayesShrink. This study enables us to generalize the BayesShrink threshold to Generalized BayesShrink which outperforms the BayesShrink itself.

Noise Variance Estimation Based on Knowledge of Noise Reduction Coefficient

2011 ◽  
Vol 340 ◽  
pp. 149-155
Author(s):  
Peksinski Jakub ◽  
Mikolajczak Grzegorz
Keyword(s):  
Image Analysis ◽  
Noise Reduction ◽  
Normal Distribution ◽  
Correlation Coefficient ◽  
Variance Reduction ◽  
Variance Estimation ◽  
Estimation Method ◽  
The Other ◽  
Noise Variance ◽  
Reduction Coefficient

The paper presents interference estimation method involving search for “flat” area, on the basis of image analysis taking into account correlation coefficient and knowledge of variance reduction coefficient for noise in the filtered area. The proposed method was tested for an interference characterised by normal distribution, and then compared to the other ones.

scholarly journals Joint adjustment by CPIII precision trigonometric elevation control network assisted with precision levelling observation

2020 ◽  
Vol 165 ◽  
pp. 03005
Author(s):  
Li Jianzhang
Keyword(s):  
Variance Estimation ◽  
Estimation Method ◽  
Parameter Method ◽  
Control Network ◽  
Network Construction

Using the precision trigonometric elevation instead of the precision levelling to build a CPⅢ elevation control network will greatly increase the speed of CPⅢ control network construction. However, the accuracy of CPIII precision trigonometric elevation control network is still difficult to reach the level of CPⅢ precision levelling network. Based on the existing parameter method, this paper introduces some precision levelling for joint adjustment, and uses Helmert’s variance estimation method to perform strict weight determination. Our experiments show that when the number of precision levelling participating in the joint adjustment exceeds 1/3 of the total number of CPⅢ precision levelling network observations, the accuracy of the CPIII precision trigonometric elevation control network can be effectively improved.

Maximum-Likelihood, Magnitude-Based, Amplitude and Noise Variance Estimation

2021 ◽  
pp. 1-1
Author(s):  
Ming-Wei Wu ◽  
Yan Jin ◽  
Yan Li ◽  
Tianyu Song ◽  
Pooi Yuen Kam
Keyword(s):  
Maximum Likelihood ◽  
Variance Estimation ◽  
Noise Variance

scholarly journals Sleep Duration and Waking Activities in Relation to the National Sleep Foundation’s Recommendations: An Analysis of US Population Sleep Patterns from 2015 to 2017

2021 ◽  
Vol 18 (11) ◽  
pp. 6154
Author(s):  
Michael Osei Mireku ◽  
Alina Rodriguez
Keyword(s):  
Sleep Duration ◽  
Time Use ◽  
Variance Estimation ◽  
Estimation Method ◽  
Short Sleep Duration ◽  
Weighted Estimates ◽  
American Time Use Survey ◽  
Increased Risk ◽  
The Us ◽  
Time Use Survey

The objective was to investigate the association between time spent on waking activities and nonaligned sleep duration in a representative sample of the US population. We analysed time use data from the American Time Use Survey (ATUS), 2015–2017 (N = 31,621). National Sleep Foundation (NSF) age-specific sleep recommendations were used to define recommended (aligned) sleep duration. The balanced, repeated, replicate variance estimation method was applied to the ATUS data to calculate weighted estimates. Less than half of the US population had a sleep duration that mapped onto the NSF recommendations, and alignment was higher on weekdays (45%) than at weekends (33%). The proportion sleeping longer than the recommended duration was higher than those sleeping shorter on both weekdays and weekends (p < 0.001). Time spent on work, personal care, socialising, travel, TV watching, education, and total screen time was associated with nonalignment to the sleep recommendations. In comparison to the appropriate recommended sleep group, those with a too-short sleep duration spent more time on work, travel, socialising, relaxing, and leisure. By contrast, those who slept too long spent relatively less time on each of these activities. The findings indicate that sleep duration among the US population does not map onto the NSF sleep recommendations, mostly because of a higher proportion of long sleepers compared to short sleepers. More time spent on work, travel, and socialising and relaxing activities is strongly associated with an increased risk of nonalignment to NSF sleep duration recommendations.

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