Adriana Vlad

Statistical noise simulation for image processing purposes

There are several image processing algorithms for enhancement, restoration, segmentation or feature extraction that assume known statistical distribution for the noise from a corrupted image. Usually this noise is considered uncorrelated, but there are many cases where the strength of a certain algorithm must be tested for spatially correlated noise. Our main point is to derive a method for controlling the autocorrelation function of gamma distributed noise images. The exponential case is included. An uncorrelated noise generating algorithm that yields Gaussian, exponential and gamma distributed images of a good statistical quality is also presented.

Logistic map sensitivity to control parameter and its implications in the statistical behaviour

This paper is a statistical study which aims to highlight aspects related to how the sensitivity of logistic map to the control parameter is reflected in its statistical behavior. Are followed the probability law and skewness issues when control parameter values are in a vicinity of 4. The analysis involves a study concerning statistical dependence between mean and variance for logistic function when control parameter value varies near 4.

Random-signal simulation for image processing applications

The main point of the paper is to discuss the control of the correlation for gamma distributed noise images. There is also presented an algorithm of generating uncorrelated noise images of a good statistical quality, with Gaussian, exponential, gamma and Rayleigh distribution.