Author:
A. Histace
Affiliation:
ETIS UMR CNRS 8051 and ENSEA-UCP, France
Keyword(s):
Stochastic resonance, Low-level image processing, Binarization.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
It is progressively realized that noise can play a constructive role in nonlinear formation processes. The starting point of the investigation of such useful noise effect has been the study of the Stochastic Resonance (SR) effect. The goal of this article is to propose a direct application of SR phenomenon in image processing, for the interest of SR in that domain is growing-up. As a prolongation of previous work already presented in the literature by author, we propose to quantitatively show that a purposely injection of a gaussian noise in a
classical nonlinear image process, as image binarization, can play a constructive action. This work can also be interpreted as a first step for a better understanding of SR in image processing relating it to classical results obtained in a nonlinear signal processing framework for classical low-level image processing tool.