How to extract useful information from noised image is always an important issue for image processing. Many methods have been proposed in image enhancement field. However, in these methods the noise is usually considered as harmful and should be removed as much as possible. Stochastic resonance is a very different method, in which the noise is regarded as a driver to push the stochastic resonance system to output enhanced image. In this paper, the cumulative gain is introduced and the sequence average is used to enhance the original image information which hidden in a noised image sequence produced by bistable stochastic resonance. We present the one-dimensional and two-dimensional stochastic resonance methods and discuss their performance in this paper. Experiments illustrate that the one-dimensional average stochastic resonance has the best performance considering the indicator PSNR and SSIM. Compared with traditional filters such as median and Wiener filters, the proposed methods have significant advantages.

如何从噪声图像中提取有用信息一直是图像处理的重要课题。在图像增强领域已经提出了许多方法。但是，在这些方法中，噪声通常被认为是有害的，应尽可能地消除。随机共振是一种非常不同的方法，其中噪声被视为推动随机共振系统输出增强图像的驱动器。本文介绍了累积增益，并使用序列平均来增强隐藏在由双稳态随机共振产生的噪声图像序列中的原始图像信息。本文介绍了一维和二维随机共振方法，并讨论了它们的性能。实验表明，考虑指标PSNR和SSIM，一维平均随机共振具有最佳性能。与传统的滤波器（例如中值和维纳滤波器）相比，该方法具有明显的优势。