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 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 the application of stochastic resonance, and it might be useful for explaining the biophysical mechanism behind visual perception.

中文翻译：

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使用尖峰神经网络的基于随机共振的视觉感知

我们的目标是提出一种有效的算法，基于集成和激发神经元的全局反馈尖峰网络中的随机共振原理来增强暗图像的对比度。通过线性近似和直接模拟，我们揭示了峰值信噪比对尖峰阈值和反馈耦合强度的依赖性。在此理论分析的基础上，我们开发了一种用于增强暗图像的动态系统算法。在新算法中，给出了如何为待增强图像选择合适的尖峰阈值的明确公式，并使用更有效的量化指标图像方差来代替常用的度量。数值试验验证了新算法的有效性。