Gaussian Noise Estimation in Digital Images Using Nonlinear Sharpening and Genetic Optimization

Author

Russo, Fabrizio

Author_Institution

Univ. of Trieste Via A., Trieste

fYear

2007

fDate

1-3 May 2007

Firstpage

1

Lastpage

5

Abstract

A new approach to estimation of Gaussian noise in digital images is presented. As a first step, a nonlinear amplification of the noise is provided by adopting a multiparameter piecewise linear (PWL) sharpener. Thus, the noise is estimated by analyzing the edge gradients of the data filtered by a PWL smoother. The optimal parameter values for the sharpening stage are found by resorting to a simple genetic algorithm and a set of training data. Computer simulations show that the approach gives accurate results in a wide range of noise variances.

Keywords

Gaussian noise; amplification; image processing; optimisation; Gaussian noise estimation; digital images; genetic optimization; multiparameter piecewise linear; nonlinear amplification; nonlinear sharpening; Computer simulation; Digital images; Gaussian noise; Genetic algorithms; Noise measurement; Noise reduction; Nonlinear filters; Piecewise linear techniques; Signal to noise ratio; Training data; Noise estimation; genetic algorithms; image processing; nonlinear filters;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE

Conference_Location

Warsaw

ISSN

1091-5281

Print_ISBN

1-4244-0588-2

Type

conf

DOI

10.1109/IMTC.2007.379092

Filename

4258354