Fu Yuxuan, Kang Yanmei, Chen Guanrong
Department of Applied Mathematics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China.
Front Comput Neurosci. 2020 May 15;14:24. doi: 10.3389/fncom.2020.00024. eCollection 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 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.
我们的目标是基于积分发放神经元的全局反馈脉冲神经网络中的随机共振原理，提出一种增强暗图像对比度的高效算法。通过线性近似和直接模拟，我们揭示了峰值信噪比与脉冲发放阈值和反馈耦合强度之间的依赖关系。基于这一理论分析，我们进而开发了一种用于增强暗图像的动态系统算法。在新算法中，给出了一个关于如何为待增强图像选择合适的脉冲发放阈值的显式公式，并且使用了一个更有效的量化指标——图像方差，来取代常用的度量。数值测试验证了新算法的有效性。该研究为随机共振的应用提供了一个很好的例子，并且可能有助于解释视觉感知背后的生物物理机制。
