Iterative Stochastic Resonance Model for Visual Information Extraction from Noisy Environment

Abstract

Stochastic Resonance phenomenon brings us a new viewpoint of the relation between noise and information which considers the noise as an interacting factor with the information. In this paper, on the Stochastic Resonance phenomenon of neurons in the human visual system, we propose a new model called Iterative Stochastic Resonance, for the visual information extraction from noisy images. The algorithm introduces appropriate noise into the noisy image so that the signal and noise produce a synergistic effect, thereby increasing the energy of the useful signal. The model is then modeled on the characteristics of the human visual system and the results are iteratively computed several times. The model can give perfect denoising output for both simulated and real laser speckle contrast images which are both disturbed by strong noise. It is a new way to solve the problem of effective information extraction in medical noisy images.