一种应用于图像去噪的广义回归神经网络模型设计研究

摘要

为了更好地去除图像中的椒盐噪声、保留图像细节信息,提出了一种广义回归神经网络模型,适用于图像去噪.首先,对传统广义回归神经网络的原理进行了分析,并对采用的广义回归神经网络进行具体设计.然后对广义回归神经网络中的唯一可调参数(平滑因子)进行了优化.采用归一化均方误差和峰值信噪比指标进行具体算法性能分析.仿真试验结果显示:相比径向基神经网络和传统广义回归神经网络,提出算法的去噪能力更强,具有较高的峰值信噪比和较低的归一化均方误差,验证了提出算法的有效性和先进性.%In order to remove the salt and pepper noise in the image and preserve the image detail information,a generalized regression neural network model is proposed,which is suitable for image denoising. Firstly,the principle of the traditional generalized recurrent neural network is analyzed,and the generalized regression neural network is designed. Then,the unique adjustable parameter (smoothing factor) in the generalized regression neural network is optimized. The normalized mean square error and peak SNR are used to analyze the performance of the proposed algorithm. The simulation results show that the proposed algorithm has stronger denoising ability,higher peak signal-noise ratio and lower normalized mean square error compared with RBF neural network and traditional generalized regression neural network,which verifies the effectiveness and advancement of the proposed algorithm.