基于饱和系统随机共振的图像去噪算法

利用随机共振（SR）机制，在传输相关信号的非线性系统中加入噪声，在输出端信号可被增强。本文提出一种基于动态饱和非线性系统随机共振的图像去噪算法，首先将图像重采样为一维信号，并调节动态饱和非线性系统的参数，使之达到最优，使动态饱和非线性系统能够产生随机共振。相比一维随机共振，二维随机共振的图像复原效果更接近于原图，输出图像的直方图和峰值信噪比(PSNR)，也明显优于一维随机共振。相比于传统滤波方法，饱和系统的去噪效果更好，同时对于噪声强度的变化具有较好的鲁棒性。

#br# Image Denoising Algorithm Based on Stochastic Resonance of Saturating System

Stochastic resonance (SR) is a mechanism that noise can be added to the nonlinear system for transmitting the certain signals, and the output can be enhanced. In this paper, we propose an image denoising algorithm based on stochastic resonance of dynamical saturating nonlinear systems. Firstly, the image is resampled into one-dimensional signals, and the parameters of the saturating nonlinear system can be tuned optimally, then the SR effect can be obtained in the saturating nonlinear system. Comparing with the effect of the one-dimensional SR, the effect of image restoration of the two-dimensional SR is closer to the original image and it is showed that the effect of image enhancement of the two-dimensional SR is superior by the output histogram and peak signal-to-noise ratio (PSNR). Compared with traditional filtering methods, the denoising effect of the saturating nonlinear system is better and it is more robust to the change of noise intensity.