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Stochastic Resonance Based Visual Perception Using Spiking Neural Networks.

Yuxuan Fu1, Yanmei Kang1, Guanrong Chen2

  • 1Department of Applied Mathematics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.

Frontiers in Computational Neuroscience
|June 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient algorithm using stochastic resonance in spiking neural networks to enhance dark image contrast. The method optimizes parameters for improved image quality and offers insights into visual perception mechanisms.

Keywords:
contrast enhancementspiking networksstochastic resonancevariance of imagevisual perception

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Area of Science:

  • Computational Neuroscience
  • Image Processing
  • Biophysics

Background:

  • Enhancing contrast in dark images is challenging.
  • Stochastic resonance (SR) is a phenomenon where noise can improve signal detection.
  • Spiking neural networks (SNNs) offer a biologically plausible framework for information processing.

Purpose of the Study:

  • To develop an efficient algorithm for dark image contrast enhancement.
  • To leverage the principle of stochastic resonance in a global feedback spiking neural network.
  • To provide a theoretical understanding and practical application of SR in image processing.

Main Methods:

  • Utilizing a global feedback spiking network of integrate-and-fire neurons.
  • Employing linear approximation and direct simulation to analyze system dynamics.
  • Developing a dynamical system algorithm based on theoretical findings.

Main Results:

  • Disclosed the dependence of peak signal-to-noise ratio (PSNR) on spiking threshold and feedback coupling strength.
  • Developed an explicit formula for selecting an optimal spiking threshold.
  • Demonstrated improved performance using image variance as a quantifying index.

Conclusions:

  • The proposed algorithm efficiently enhances dark image contrast.
  • The study validates the application of stochastic resonance in image enhancement.
  • Findings may contribute to understanding the biophysical mechanisms of visual perception.