As an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

作为智慧城市建设的重要组成部分，交通图像去噪得到了广泛的研究。图像去噪技术可以提高分割识别模型的性能，提高分割识别结果的准确性。然而，由于噪声的类型和噪声污染程度的不同，传统的图像去噪方法普遍存在边缘和细节模糊、图像信息丢失等问题。本文提出了一种基于改进鲸鱼优化算法优化的BP神经网络的图像去噪方法。首先在算法中引入非线性收敛因子和自适应权重系数，提高标准鲸鱼优化算法的优化能力和收敛特性。然后，采用改进的鲸鱼优化算法优化BP神经网络的初始权重和阈值，克服构建过程中的依赖性，缩短神经网络的训练时间。最后，将优化后的BP神经网络应用于基准图像去噪和交通图像去噪。实验结果表明，与传统的中值滤波、邻域平均滤波和维纳滤波等去噪方法相比，该方法在峰值信噪比方面具有更好的性能。优化后的BP神经网络应用于基准图像去噪和交通图像去噪。实验结果表明，与传统的中值滤波、邻域平均滤波和维纳滤波等去噪方法相比，该方法在峰值信噪比方面具有更好的性能。优化后的BP神经网络应用于基准图像去噪和交通图像去噪。实验结果表明，与传统的中值滤波、邻域平均滤波和维纳滤波等去噪方法相比，该方法在峰值信噪比方面具有更好的性能。