Stochastic Resonance Based Visual Perception Using Spiking Neural Networks - Fu, Yuxuan; Kang, Yanmei; Chen, Guanrong (Frontiers in Computational Neuroscience, 2020)
Our aim is to propose an efficient algorithm for enhancing the contrast of dark images based on the principle of stochastic resonance in a global feedback spiking network of integrate-and-fire neurons. By linear approximation and direct simulation, we disclose the dependence of the peak signal-to-noise ratio on the spiking threshold and the feedback coupling strength. Based on this theoretical analysis, we then develop a dynamical system algorithm for enhancing dark images. In the new algorithm, an explicit formula is given on how to choose a suitable spiking threshold for the images to be enhanced, and a more effective quantifying index, the variance of the image, is used to replace the commonly used measure. Numerical tests verify the efficiency of the new algorithm. The investigation provides a good example for application of stochastic resonance and it might also be useful for exploring the biophysical mechanism behind visual perception.