scholarly journals Providing a New Approach for Modeling and Parameter Estimation of Probability Density Function of Noise in Digital Images

2015 ◽  
Vol 37 ◽  
pp. 371
Author(s):  
Hanif Yaghoobi ◽  
Keivan Maghooli ◽  
Alireza Ghahramani Barandagh
Keyword(s):  
Parameter Estimation ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Digital Images ◽  
New Approach

scholarly journals Providing a New Approach for Modeling and Parameter Estimation of Probability Density Function of Noise in Digital Images

2015 ◽  
Vol 37 ◽  
pp. 182
Author(s):  
Hanif Yaghoobi ◽  
Keivan Maghooli ◽  
Alireza Ghahramani Barandagh
Keyword(s):  
Parameter Estimation ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Digital Images ◽  
Image Sensors ◽  
Arrival Process ◽  
Mean And Variance ◽  
Two Parameters ◽  
Generalized Probability

The main part of the noise in digital images arises when taking pictures or transmission. There is noise in the imagescaptured by the image sensors of the real world. Noise, based on its causes can have different probability density functions.For example, such a model is called the Poisson distribution function of the random nature of photon arrival process that isconsistent with the distribution of pixel values measured. The parameters of the noise probability density function (PDF)can be achieved to some extent the properties of the sensor. But, we need to estimate the parameters for imaging settings. Ifwe assume that the PDF of noise is approximately Gaussian, then we need only to estimate the mean and variance becausethe Gaussian PDF with only two parameters is determined. In fact, in many cases, PDF of noise is not Gaussian and it hasunknown distribution. In this study, we introduce a generalized probability density function for modeling noise in imagesand propose a method to estimate its parameters. Because the generalized probability density function has multipleparameters, so use common parameter estimation techniques such as derivative method to maximize the likelihood functionwould be extremely difficult. In this study, we propose the use of evolutionary algorithms for global optimization. Theresults show that this method accurately estimates the probability density function parameters.

scholarly journals A particle-filter framework for robust cryoEM 3D reconstruction

2018 ◽  
Author(s):  
Mingxu Hu ◽  
Hongkun Yu ◽  
Kai Gu ◽  
Kunpeng Wang ◽  
Siyuan Ren ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Probability Density Function ◽  
Particle Filter ◽  
Probability Density ◽  
Density Function ◽  
Sequential Monte Carlo ◽  
High Dimensional ◽  
Dimensional Parameter ◽  
Support Points ◽  
Estimated Parameters

AbstractElectron cryo-microscopy (cryoEM) is now a powerful tool in determining atomic structures of biological macromolecules under nearly natural conditions. The major task of single-particle cryoEM is to estimate a set of parameters for each input particle image to reconstruct the three-dimensional structure of the macromolecules. As future large-scale applications require increasingly higher resolution and automation, robust high-dimensional parameter estimation algorithms need to be developed in the presence of various image qualities. In this paper, we introduced a particle-filter algorithm for cryoEM, which was a sequential Monte Carlo method for robust and fast high-dimensional parameter estimation. The cryoEM parameter estimation problem was described by a probability density function of the estimated parameters. The particle filter uses a set of random and weighted support points to represent such a probability density function. The statistical properties of the support points not only enhance the parameter estimation with self-adaptive accuracy but also provide the belief of estimated parameters, which is essential for the reconstruction phase. The implementation of these features showed strong tolerance to bad particles and enabled robust defocus refinement, demonstrated by the remarkable resolution improvement at the atomic level.

A new approach to analysis of residue probability density function in pipelined ADCs

Integration ◽  
2016 ◽  
Vol 52 ◽  
pp. 51-61 ◽  
Author(s):  
Esmaeil Fatemi-Behbahani ◽  
Ebrahim Farshidi ◽  
Karim Ansari-Asl
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
New Approach ◽  
Pipelined Adcs

Reserch on a NOx Emission Forecasting Method Based on Naïve Bayesian

2014 ◽  
Vol 936 ◽  
pp. 1857-1861
Author(s):  
Zhang Lin ◽  
Wang Xin ◽  
Zhang Qi
Keyword(s):  
Diesel Engine ◽  
Probability Density Function ◽  
Environmental Pollution ◽  
Probability Density ◽  
Density Function ◽  
Nox Emission ◽  
New Approach ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Forecasting Method

Increasingly serious environmental pollution,trying to find a effective method to control NOx emission become more importance. Under this background, this paper adopts the naïve Bayesian classifier method which built on the basis of the probability density function to forecasting the NOx emission of diesel engine. This paper proposes a new approach to weight the super-parent one dependence estimators, and uses the UCI datasets to verify the validity of the proposed method. Finally, apply this diagnosis technology to the collected WD615 diesel engine data. The comparison experiments with other algorithms demonstrate the effectiveness of the proposed method.

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