Providing a New Approach for Modeling and Parameter Estimation of Probability Density Function of Noise in Digital Images
Saved in:
| Main Author: | |
|---|---|
| Format: | Artículo científico |
| Language: | en |
| Published: |
Universidade Federal de Santa Maria
2015
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866816213963767808 |
|---|---|
| author | Hanif Yaghoobi |
| author_facet | Hanif Yaghoobi |
| contents | Providing a New Approach for Modeling and Parameter Estimation of Probability Density Function of Noise in Digital Images Hanif Yaghoobi Keivan Maghooli Alireza Ghahramani Barandagh Estudios Ambientales Global Optimization Parameter Estimation Evolutionary Algorithms Noise Probability Density Function The main part of the noise in digital images arises when taking pictures or transmission. There is noise in the images captured by the image sensors of the real world. Noise, based on its causes can have different probability density functions. For exam ple, such a mode l is called the Poisson distribution function of the random nature of photon arrival process that is consistent with the distribution of pixel values measured. The parameters of the noise probability density function (PDF) can be achieved to some extent th e properties of the sensor. But, we need to estimate the parameters for imaging settings. If we assume that the PDF of noise is approximately Gaussian, then we need only to estimate the mean and variance because the Gaussian PDF with only two parameters is determined. In fact, in many cases, PDF of noise is not Gaussian and it has unknown distribution. In this study, we introduce a generalized probability density function for modeling noise in images and propose a method to estimate its parameters. Becaus e the generalized probability density function has multiple parameters, so use common parameter estimation techniques such as derivative method to maximize the likelihood function would be extremely difficult. In this study, we propose the use of evol utionar y algorithms for global optimization. The results show that this method accurately estimates the probability density function parameters. 2015 artículo científico 0100-8307 https://www.redalyc.org/articulo.oa?id=467547683049 en http://www.redalyc.org/revista.oa?id=4675 Ciência e Natura application/pdf Universidade Federal de Santa Maria Ciência e Natura (Brasil) Num.6-2 Vol.37 |
| format | Artículo científico |
| id | redalyc_467547683049 |
| language | en |
| publishDate | 2015 |
| publisher | Universidade Federal de Santa Maria |
| spellingShingle | Providing a New Approach for Modeling and Parameter Estimation of Probability Density Function of Noise in Digital Images Hanif Yaghoobi Estudios Ambientales Global Optimization Parameter Estimation Evolutionary Algorithms Noise Probability Density Function Providing a New Approach for Modeling and Parameter Estimation of Probability Density Function of Noise in Digital Images Hanif Yaghoobi Keivan Maghooli Alireza Ghahramani Barandagh Estudios Ambientales Global Optimization Parameter Estimation Evolutionary Algorithms Noise Probability Density Function The main part of the noise in digital images arises when taking pictures or transmission. There is noise in the images captured by the image sensors of the real world. Noise, based on its causes can have different probability density functions. For exam ple, such a mode l is called the Poisson distribution function of the random nature of photon arrival process that is consistent with the distribution of pixel values measured. The parameters of the noise probability density function (PDF) can be achieved to some extent th e properties of the sensor. But, we need to estimate the parameters for imaging settings. If we assume that the PDF of noise is approximately Gaussian, then we need only to estimate the mean and variance because the Gaussian PDF with only two parameters is determined. In fact, in many cases, PDF of noise is not Gaussian and it has unknown distribution. In this study, we introduce a generalized probability density function for modeling noise in images and propose a method to estimate its parameters. Becaus e the generalized probability density function has multiple parameters, so use common parameter estimation techniques such as derivative method to maximize the likelihood function would be extremely difficult. In this study, we propose the use of evol utionar y algorithms for global optimization. The results show that this method accurately estimates the probability density function parameters. 2015 artículo científico 0100-8307 https://www.redalyc.org/articulo.oa?id=467547683049 en http://www.redalyc.org/revista.oa?id=4675 Ciência e Natura application/pdf Universidade Federal de Santa Maria Ciência e Natura (Brasil) Num.6-2 Vol.37 |
| title | Providing a New Approach for Modeling and Parameter Estimation of Probability Density Function of Noise in Digital Images |
| topic | Estudios Ambientales Global Optimization Parameter Estimation Evolutionary Algorithms Noise Probability Density Function |
| url | https://www.redalyc.org/articulo.oa?id=467547683049 |